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Pipeline run

03d67674-302b-4021-823d-f10b7313a39d

Pipeline LLM cost (USD)
API 1: $0.0003 API 2: $0.0004 API 3: $0.0000 Total: $0.0007

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
role baseline loaded sources · ai_index: jd · nature_of_work: no_kras · tech_stack_maturity: jd
Nature of work no kras
Vague JD — no KRAs present to derive a specific nature of work.
Tech stack maturity
Mainstream Modern
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
0.20 / 5
· Title match
Has AI skill
· AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3): RAG, LLMs, MLOps, NLP, AI, ML, AI/ML, Generative AI, Machine Learning, Deep Learning
Evidence — skills matched in JD (15)
Python TensorFlow PyTorch Scikit-learn REST Docker Kubernetes SQL NoSQL AWS GCP Git CI/CD MLflow Kubeflow
Skill cluster (7 dimension groups, role-scoped)
Cloud Provider Platforms
AWS GCP
Container Orchestration Platforms
Kubernetes
Containerization and Image Builds
Docker
Integration Protocols & Standards
REST
ML Frameworks and Libraries
PyTorch
Python Programming
Python
Cross-cutting / unaligned
TensorFlow Scikit-learn SQL NoSQL Git CI/CD MLflow Kubeflow
Status: completed Created: 2026-05-12T11:29:04.559832Z Updated: 2026-05-12T11:29:06.663776Z API 3 duration: 434 ms
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

Data Scientist

slug: data-scientist · id: 7 · source: db

The primary skill of Python aligns well with the role of a Data Scientist, which also utilizes additional skills in machine learning frameworks, making it a suitable match.

Resolution: in_db — role exists in library; skill↔dim and role↔dim links saved when applicable.

0
New skills
0
Skill↔dim saved
0
Role↔dim saved
0
Skipped

Job description

Job Description – Machine Learning Engineer

Experience: 2–5 Years
Location: Bangalore / Hybrid
Employment Type: Full-Time

About the Role

We are seeking a Machine Learning Engineer to build and deploy scalable AI/ML solutions for real-world applications. You will work closely with data scientists, backend engineers, and product teams to develop intelligent systems and production-grade ML pipelines.

Key Responsibilities
Design, train, and optimize machine learning models
Build scalable ML pipelines for data processing and inference
Deploy ML models using cloud and container technologies
Work with large structured and unstructured datasets
Improve model accuracy, latency, and reliability
Integrate AI services into backend applications and APIs
Monitor model performance and retrain models when required
Collaborate with cross-functional teams on AI features
Required Skills
Strong knowledge of Python and machine learning libraries
Experience with TensorFlow, PyTorch, or Scikit-learn
Understanding of deep learning and NLP concepts
Experience with REST APIs and backend integration
Familiarity with Docker and Kubernetes
Knowledge of SQL/NoSQL databases
Understanding of cloud platforms such as AWS or GCP
Experience with Git and CI/CD workflows
Preferred Qualifications
Experience with LLMs or Generative AI
Knowledge of MLOps tools like MLflow or Kubeflow
Familiarity with vector databases and RAG pipelines
Exposure to distributed systems and big data tools
Benefits
Flexible hybrid work model
AI research and learning budget
Health and wellness benefits
Fast-paced product engineering environment
Opportunity to work on cutting-edge AI systems

Skills from this JD

Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.

Python Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Python id=393 · python

Aliases — catalog

  • Cobalt Strike (CANONICAL) primary

Context tags (catalog)

Malleable C2 beacon credential dumping kerberos lateral movement payload phishing post-exploitation privilege escalation psexec red team sleep mask smb stager team server

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Adversary Simulation Tool
Vendor
Fortra
License
proprietary
Year introduced
2012
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Appears in a limited set of red-team/pentest JDs and security vendor training, but far below mainstream devops tools; market signal is specialized adversary-simulation usage rather than broad hiring demand.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
54
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Analytical Programming Languages Catalog dimension db id 82

    Library dimension (catalog)

    Roles linked in library: Data Analyst, Data Scientist

  • Automation Scripting and CLI Catalog dimension db id 48

    Library dimension (catalog)

    Roles linked in library: Azure Cloud Engineer, Cloud Engineer

  • Automation and Scripting for Operations Catalog dimension db id 361

    Library dimension (catalog)

    Roles linked in library: Virtualization Engineer

  • Network Automation and Scripting Catalog dimension db id 285

    Library dimension (catalog)

    Roles linked in library: Network Engineer

  • Programming Languages for AI Workflows Catalog dimension db id 261

    Library dimension (catalog)

    Roles linked in library: AI Engineer

  • Programming Languages for Backend Systems Catalog dimension db id 140

    Library dimension (catalog)

    Roles linked in library: Backend Engineer

  • Programming Languages for Data Work Catalog dimension db id 67

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Programming Languages for ML Systems Catalog dimension db id 113

    Library dimension (catalog)

    Roles linked in library: Machine Learning Engineer

  • Programming Languages for Security Work Catalog dimension db id 328

    Library dimension (catalog)

    Roles linked in library: Cybersecurity Engineer

  • Programming Languages for Test Automation Catalog dimension db id 193

    Library dimension (catalog)

    Roles linked in library: Automation Tester

  • Security Automation and Scripting Catalog dimension db id 258

    Library dimension (catalog)

    Roles linked in library: Cybersecurity Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Analytical Programming Languages
analytical-programming-languages
Existing dimension (library) · Role↔dimension saved
Automation Scripting and CLI
automation-scripting-and-cli
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Automation and Scripting for Operations
automation-and-scripting-for-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Network Automation and Scripting
network-automation-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for AI Workflows
programming-languages-for-ai-workflows
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Backend Systems
programming-languages-for-backend-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Security Work
programming-languages-for-security-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Test Automation
programming-languages-for-test-automation
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Security Automation and Scripting
security-automation-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
TensorFlow Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: TensorFlow id=558 · tensorflow

Aliases — catalog

  • shader graphs (CANONICAL) primary

Context tags (catalog)

GLSL HLSL PBR UV mapping fragment shader material editor node graph node-based normal map procedural texturing render pipeline shader compiler tessellation vertex shader visual scripting

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Visual Shader Authoring Framework
Vendor
Unity Technologies
License
proprietary
Year introduced
2018
Confidence
0.74
Version strategy
NOT_APPLICABLE

Maturity reasoning: Shader graphs appear in some Unity/Unreal and VFX job postings, but JD volume is far below core graphics skills like HLSL/GLSL; market use is concentrated in game/real-time rendering teams.

Skill profile (library / DB)

Skill nature
LIBRARY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
456
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Applied Machine Learning Toolkits Catalog dimension db id 94

    Library dimension (catalog)

    Roles linked in library: Data Scientist

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Applied Machine Learning Toolkits
applied-machine-learning-toolkits
Existing dimension (library) · Role↔dimension saved
PyTorch Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: PyTorch id=557 · pytorch

Aliases — catalog

  • GLSL (CANONICAL) primary

Context tags (catalog)

GPU HLSL OpenGL SPIR-V Vulkan WebGL compute shader fragment shader rendering pipeline shader shader pipeline texture sampling uniform varying vertex shader

Stored enrichment (catalog DB)

Category
Language
Sub-category
Shader Language
Vendor
Khronos Group
License
other_open
Year introduced
2004
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: GLSL appears in graphics/game-engine JDs but at much lower volume than mainstream languages; it’s specialized for shader programming and often replaced in newer pipelines by HLSL/Metal Shading Language or higher-level abstractions.

Skill profile (library / DB)

Skill nature
LIBRARY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
456
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Applied Machine Learning Toolkits Catalog dimension db id 94

    Library dimension (catalog)

    Roles linked in library: Data Scientist

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Applied Machine Learning Toolkits
applied-machine-learning-toolkits
Existing dimension (library) · Role↔dimension saved
Scikit-learn Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: scikit-learn id=554 · scikit-learn

Aliases — catalog

  • post-processing (CANONICAL) primary

Context tags (catalog)

GPU anti-aliasing bloom color grading compositing depth of field fragment shader framebuffer image filtering motion blur render pipeline render target screen-space shader tone mapping

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Graphics Effect Concept
Confidence
0.86
Version strategy
NOT_APPLICABLE

Maturity reasoning: Job postings rarely list "post-processing" as a standalone skill; it appears mainly in graphics/VFX roles, while broader JDs usually specify tools like Unreal/Unity or Photoshop instead.

Skill profile (library / DB)

Skill nature
LIBRARY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
458
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Applied Machine Learning Toolkits Catalog dimension db id 94

    Library dimension (catalog)

    Roles linked in library: Data Scientist

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Applied Machine Learning Toolkits
applied-machine-learning-toolkits
Existing dimension (library) · Role↔dimension saved
REST Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: REST id=121 · rest

Aliases — catalog

  • DStreams (VERSION)
  • Spark 2.x (VERSION)
  • Spark 3.x (VERSION)
  • Spark Streaming (VERSION)
  • Spark Structured Streaming (VERSION)
  • Structured Streaming (VERSION)

Context tags (catalog)

DStreams Kafka Kinesis Structured Streaming backpressure checkpointing event time exactly-once micro-batch stateful processing streaming ETL trigger intervals watermarking window functions windowing

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Stream Processing Framework
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2013
Confidence
0.90
Version strategy
SEPARATE_ENTITY
Version tag
Structured Streaming (Spark 2.0+)

Maturity reasoning: JD volume is far lower than Structured Streaming; most Spark streaming roles now specify Structured Streaming or Kafka/Flink, and Spark docs position Spark Streaming as the older API.

Skill profile (library / DB)

Skill nature
PROTOCOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
67
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • API Integration and Data Fetching Catalog dimension db id 9

    Library dimension (catalog)

    Roles linked in library: Frontend Engineer, Full Stack Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
API Integration and Data Fetching
api-integration-and-data-fetching
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Docker Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Docker id=153 · docker

Aliases — catalog

  • Metabase (CANONICAL) primary

Context tags (catalog)

BigQuery MySQL PostgreSQL Redshift SQL ad hoc analysis cards collections dashboards data visualization embedded analytics filters questions segments self-service BI

Stored enrichment (catalog DB)

Category
Tool
Sub-category
Bi Analytics Tool
Vendor
Metabase, Inc.
License
apache_2
Year introduced
2014
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Metabase appears in many BI/analytics job postings and is growing in GitHub usage, but it is still far less universal than Tableau/Power BI in enterprise JDs.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
170
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Containerization and Image Delivery Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

  • Model Serving Deployment and Runtime Packaging Catalog dimension db id 52

    Library dimension (catalog)

    Roles linked in library: MLOps Engineer, Machine Learning Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Containerization and Image Delivery
containerization-and-image-delivery
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Model Serving Deployment and Runtime Packaging
model-serving-deployment-and-runtime-packaging
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kubernetes Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Kubernetes id=158 · kubernetes

Aliases — catalog

  • Column-level security (CANONICAL) primary

Context tags (catalog)

ABAC PII access policies attribute-based access control audit logging data governance data masking database permissions dynamic masking fine-grained access control least privilege policy enforcement row-level security sensitive data static masking

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Access Control Concept
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Appears in cloud/data platform JDs and vendor docs for Snowflake, BigQuery, and PostgreSQL RLS/column masking, but is not yet a universal hiring staple like core IAM or RBAC.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
1524
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Orchestration Platforms Catalog dimension db id 25

    Library dimension (catalog)

    Roles linked in library: Cloud Engineer, DevOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Orchestration Platforms
orchestration-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: SQL id=2601 · sql

Aliases — from this run (catalog unavailable)

  • SQL (CANONICAL)

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
55
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Relational Data Modeling Catalog dimension db id 71

    Library dimension (catalog)

    Roles linked in library: Backend Engineer, Data Engineer

  • Version Control Systems Catalog dimension db id 365

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Version Control Systems
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
NoSQL Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: NoSQL id=2639 · nosql

Aliases — from this run (catalog unavailable)

  • NoSQL (CANONICAL)

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
2150
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Data Access and Query Optimization Catalog dimension db id 74

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Data Access and Query Optimization
data-access-and-query-optimization
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: AWS id=163 · aws

Aliases — catalog

  • Compaction (CANONICAL) primary

Context tags (catalog)

Bloom filter LSM tree SSTable checkpointing defragmentation garbage collection leveling log-structured merge policy segment merge storage engine tiered compaction tombstones vacuum write amplification

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Storage Maintenance Concept
Confidence
0.74
Version strategy
NOT_APPLICABLE

Maturity reasoning: Compaction is a standard storage-maintenance concept in widely used systems like LSM databases and Kafka; it appears in many JDs for Cassandra, RocksDB, and Kafka ops roles, indicating broad market demand.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
161
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Platform Operations Catalog dimension db id 26

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

  • Cloud Security Platforms Catalog dimension db id 332

    Library dimension (catalog)

    Roles linked in library: Cybersecurity Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Platform Operations
cloud-platform-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Security Platforms
cloud-security-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GCP Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: GCP id=2304 · gcp

Aliases — catalog

  • ASGI (CANONICAL) primary

Context tags (catalog)

ASGI app ASGI server Django Channels FastAPI HTTP/2 Starlette WebSocket application scope asyncio background tasks concurrency event loop lifespan middleware routing

Stored enrichment (catalog DB)

Category
Protocol
Sub-category
Web Application Protocol
Vendor
Django Software Foundation
License
bsd
Year introduced
2016
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: ASGI appears in many Python web JDs for async frameworks like FastAPI/Starlette, but WSGI remains the broader default in legacy stacks; market signal shows growing adoption rather than universal demand.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
13
Sub-category id
161
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Security Platforms Catalog dimension db id 332

    Library dimension (catalog)

    Roles linked in library: Cybersecurity Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Security Platforms
cloud-security-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Git Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Git id=2578 · git

Aliases — from this run (catalog unavailable)

  • Git (CANONICAL)

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
2101
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Version Control Systems Catalog dimension db id 365

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Version Control Systems
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: CI/CD id=2579 · ci-cd

Aliases — from this run (catalog unavailable)

  • CI/CD (CANONICAL)

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
7
Sub-category id
2102
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Version Control Systems Catalog dimension db id 365

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Version Control Systems
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
MLflow Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: MLflow id=2640 · mlflow

Aliases — catalog

  • effects (CANONICAL) primary

Context tags (catalog)

asynchronous effects context API data flow event handling functional programming immutable state middleware observable pure functions react reactive programming redux side effects state management state transitions

Stored enrichment (catalog DB)

Category
Concept
Sub-category
State Side Effect Concept
Confidence
0.74
Version strategy
NOT_APPLICABLE

Maturity reasoning: Effects are increasingly listed in modern frontend/state-management JDs and docs (e.g., React/Redux side-effect handling, RxJS, Effector), but there is no single universal standard or dominant hiring staple yet.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
EMERGING
Typical lifespan
EVERGREEN
Category id
11
Sub-category id
2151
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Model Serving Deployment and Runtime Packaging Catalog dimension db id 52

    Library dimension (catalog)

    Roles linked in library: MLOps Engineer, Machine Learning Engineer

  • Project Delivery and Coordination Catalog dimension db id 366

    Library dimension (catalog)

  • Version Control Systems Catalog dimension db id 365

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Model Serving Deployment and Runtime Packaging
model-serving-deployment-and-runtime-packaging
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Project Delivery and Coordination
d_init_02
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Version Control Systems
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kubeflow Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Kubeflow id=2641 · kubeflow

Aliases — catalog

  • Reactive Forms (CANONICAL) primary

Context tags (catalog)

Angular Async Validators Change Detection ControlValueAccessor Dynamic Forms FormArray FormBuilder FormControl FormGroup Observables ReactiveX State Management Template-driven User Input Handling Validation

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Forms Concept
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common in Angular job descriptions and official Angular docs; widely used for complex form handling, with no vendor sunset or replacement signal.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
4
Sub-category id
2152
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Model Serving Deployment and Runtime Packaging Catalog dimension db id 52

    Library dimension (catalog)

    Roles linked in library: MLOps Engineer, Machine Learning Engineer

  • Version Control Systems Catalog dimension db id 365

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Model Serving Deployment and Runtime Packaging
model-serving-deployment-and-runtime-packaging
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Version Control Systems
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

All API 3 persistence rows

Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.

Skill Tag Dimension Skill↔dim Role↔dim Outcome Notes
Python in_db
Analytical Programming Languages
analytical-programming-languages
Existing dimension (library) · Role↔dimension saved
Python in_db
Automation Scripting and CLI
automation-scripting-and-cli
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Automation and Scripting for Operations
automation-and-scripting-for-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Network Automation and Scripting
network-automation-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for AI Workflows
programming-languages-for-ai-workflows
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for Backend Systems
programming-languages-for-backend-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for Security Work
programming-languages-for-security-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for Test Automation
programming-languages-for-test-automation
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Security Automation and Scripting
security-automation-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
TensorFlow in_db
Applied Machine Learning Toolkits
applied-machine-learning-toolkits
Existing dimension (library) · Role↔dimension saved
PyTorch in_db
Applied Machine Learning Toolkits
applied-machine-learning-toolkits
Existing dimension (library) · Role↔dimension saved
Scikit-learn in_db
Applied Machine Learning Toolkits
applied-machine-learning-toolkits
Existing dimension (library) · Role↔dimension saved
REST in_db
API Integration and Data Fetching
api-integration-and-data-fetching
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Docker in_db
Containerization and Image Delivery
containerization-and-image-delivery
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Docker in_db
Model Serving Deployment and Runtime Packaging
model-serving-deployment-and-runtime-packaging
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kubernetes in_db
Orchestration Platforms
orchestration-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Relational Data Modeling
relational-data-modeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Version Control Systems
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
NoSQL in_db
Data Access and Query Optimization
data-access-and-query-optimization
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Cloud Platform Operations
cloud-platform-operations
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AWS in_db
Cloud Security Platforms
cloud-security-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
GCP in_db
Cloud Security Platforms
cloud-security-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Git in_db
Version Control Systems
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD in_db
Version Control Systems
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
MLflow in_db
Model Serving Deployment and Runtime Packaging
model-serving-deployment-and-runtime-packaging
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
MLflow in_db
Project Delivery and Coordination
d_init_02
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
MLflow in_db
Version Control Systems
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kubeflow in_db
Model Serving Deployment and Runtime Packaging
model-serving-deployment-and-runtime-packaging
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Kubeflow in_db
Version Control Systems
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

No artifact rows for this run.
API 1 — extract-from-jd click to toggle
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API 2 — extract-details
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        "id": 328,
        "rationale": "Languages used to automate security tasks, write detection logic, and build analysis or remediation tooling. This is the core coding surface for a cybersecurity engineer across scripts, queries, and small utilities.",
        "slug": "programming-languages-for-security-work",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cybersecurity Engineer",
          "id": 9,
          "rationale": null,
          "role_archetype": null,
          "slug": "cybersecurity-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for Test Automation",
        "id": 193,
        "rationale": "Languages used to implement automated checks, helper utilities, and test harness code. This is the core coding surface for turning test ideas into maintainable automation.",
        "slug": "programming-languages-for-test-automation",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Automation Tester",
          "id": 16,
          "rationale": null,
          "role_archetype": null,
          "slug": "automation-tester",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Security Automation and Scripting",
        "id": 258,
        "rationale": "Automating repeatable security checks, enrichment, and remediation workflows. This cluster is coherent because the role often needs lightweight automation to scale analysis and response.",
        "slug": "security-automation-and-scripting",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cybersecurity Engineer",
          "id": 9,
          "rationale": null,
          "role_archetype": null,
          "slug": "cybersecurity-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Applied Machine Learning Toolkits",
        "id": 94,
        "rationale": "Libraries and frameworks used to prototype and compare models quickly. This dimension captures the concrete tooling layer beneath modeling methods and evaluation.",
        "slug": "applied-machine-learning-toolkits",
        "source": "db"
      },
      "input_skill": "TensorFlow",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Scientist",
          "id": 7,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-scientist",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Applied Machine Learning Toolkits",
        "id": 94,
        "rationale": "Libraries and frameworks used to prototype and compare models quickly. This dimension captures the concrete tooling layer beneath modeling methods and evaluation.",
        "slug": "applied-machine-learning-toolkits",
        "source": "db"
      },
      "input_skill": "PyTorch",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Scientist",
          "id": 7,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-scientist",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Applied Machine Learning Toolkits",
        "id": 94,
        "rationale": "Libraries and frameworks used to prototype and compare models quickly. This dimension captures the concrete tooling layer beneath modeling methods and evaluation.",
        "slug": "applied-machine-learning-toolkits",
        "source": "db"
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      "input_skill": "Scikit-learn",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Scientist",
          "id": 7,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-scientist",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "API Integration and Data Fetching",
        "id": 9,
        "rationale": "Connecting frontend applications to backend services and third-party endpoints. This covers request orchestration, error handling, pagination, and shaping remote data for UI consumption.",
        "slug": "api-integration-and-data-fetching",
        "source": "db"
      },
      "input_skill": "REST",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Frontend Engineer",
          "id": 3,
          "rationale": null,
          "role_archetype": "Frontend Engineers design and build the user-facing parts of applications, translating product and design requirements into interactive experiences. They focus on how the application looks, behaves, and responds in the browser, ensuring usability, accessibility, and consistency across the interface.",
          "slug": "frontend-engineer",
          "source": "db"
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        {
          "display_name": "Full Stack Developer",
          "id": 2,
          "rationale": null,
          "role_archetype": null,
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          "source": "db"
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      ]
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        "difficulty_hint": "well_known",
        "display_name": "Containerization and Image Delivery",
        "id": 24,
        "rationale": "Builds, packages, and ships application and support workloads as container images. This cluster covers the artifact format and the mechanics of producing deployable images.",
        "slug": "containerization-and-image-delivery",
        "source": "db"
      },
      "input_skill": "Docker",
      "llm_role": null,
      "roles_from_db": [
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          "display_name": "DevOps Engineer",
          "id": 1,
          "rationale": null,
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          "slug": "devops-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Model Serving Deployment and Runtime Packaging",
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        "rationale": "Operational deployment of trained models into online, batch, or streaming serving environments, including packaging models and model servers into containers or managed inference runtimes, coordinating rollout, and handing off to inference systems. Covers serving frameworks and platforms such as TensorFlow Serving, TorchServe, Triton Inference Server, BentoML, KServe, and Seldon Core, plus container/runtime concerns like Docker images, GPU-enabled containers, base image selection, container entrypoints, runtime dependencies, and image scanning for model services.",
        "slug": "model-serving-deployment-and-runtime-packaging",
        "source": "db"
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      "input_skill": "Docker",
      "llm_role": null,
      "roles_from_db": [
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          "display_name": "MLOps Engineer",
          "id": 5,
          "rationale": null,
          "role_archetype": null,
          "slug": "mlops-engineer",
          "source": "db"
        },
        {
          "display_name": "Machine Learning Engineer",
          "id": 10,
          "rationale": null,
          "role_archetype": null,
          "slug": "machine-learning-engineer",
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        }
      ]
    },
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      "dimension": {
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        "display_name": "Orchestration Platforms",
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        "rationale": "Operates the platforms that schedule and run containerized workloads and related deployment primitives. This is separate from image delivery because it concerns runtime placement and service rollout behavior.",
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        "source": "db"
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      "input_skill": "Kubernetes",
      "llm_role": null,
      "roles_from_db": [
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          "display_name": "Cloud Engineer",
          "id": 18,
          "rationale": null,
          "role_archetype": null,
          "slug": "cloud-engineer",
          "source": "db"
        },
        {
          "display_name": "DevOps Engineer",
          "id": 1,
          "rationale": null,
          "role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
          "slug": "devops-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Relational Data Modeling",
        "id": 71,
        "rationale": "Designing tables, relationships, constraints, and transactional data shapes for operational backend systems. This cluster is coherent because backend services frequently own the canonical application data model.",
        "slug": "relational-data-modeling",
        "source": "db"
      },
      "input_skill": "SQL",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Backend Engineer",
          "id": 14,
          "rationale": null,
          "role_archetype": null,
          "slug": "backend-engineer",
          "source": "db"
        },
        {
          "display_name": "Data Engineer",
          "id": 6,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Version Control Systems",
        "id": 365,
        "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
        "slug": "d_init_01",
        "source": "db"
      },
      "input_skill": "SQL",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Data Access and Query Optimization",
        "id": 74,
        "rationale": "Techniques for making analytical data fast and reliable to query. This includes partitioning, clustering, indexing choices, file layout, and access-path tuning for downstream consumers.",
        "slug": "data-access-and-query-optimization",
        "source": "db"
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      "input_skill": "NoSQL",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
          "id": 6,
          "rationale": null,
          "role_archetype": null,
          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Platform Operations",
        "id": 26,
        "rationale": "Uses cloud provider services to support delivery and runtime environments. The focus is on consumer-level operation of cloud services rather than deep cloud architecture ownership.",
        "slug": "cloud-platform-operations",
        "source": "db"
      },
      "input_skill": "AWS",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "DevOps Engineer",
          "id": 1,
          "rationale": null,
          "role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
          "slug": "devops-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Security Platforms",
        "id": 332,
        "rationale": "Cloud-native security products used to assess posture, detect misconfigurations, and monitor workloads across AWS, Azure, and GCP. This is a distinct product family because the role often works across multiple CNAPP/CSPM/CWPP offerings and cloud-native detectors.",
        "slug": "cloud-security-platforms",
        "source": "db"
      },
      "input_skill": "AWS",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cybersecurity Engineer",
          "id": 9,
          "rationale": null,
          "role_archetype": null,
          "slug": "cybersecurity-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Security Platforms",
        "id": 332,
        "rationale": "Cloud-native security products used to assess posture, detect misconfigurations, and monitor workloads across AWS, Azure, and GCP. This is a distinct product family because the role often works across multiple CNAPP/CSPM/CWPP offerings and cloud-native detectors.",
        "slug": "cloud-security-platforms",
        "source": "db"
      },
      "input_skill": "GCP",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cybersecurity Engineer",
          "id": 9,
          "rationale": null,
          "role_archetype": null,
          "slug": "cybersecurity-engineer",
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        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Version Control Systems",
        "id": 365,
        "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
        "slug": "d_init_01",
        "source": "db"
      },
      "input_skill": "Git",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Version Control Systems",
        "id": 365,
        "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
        "slug": "d_init_01",
        "source": "db"
      },
      "input_skill": "CI/CD",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Model Serving Deployment and Runtime Packaging",
        "id": 52,
        "rationale": "Operational deployment of trained models into online, batch, or streaming serving environments, including packaging models and model servers into containers or managed inference runtimes, coordinating rollout, and handing off to inference systems. Covers serving frameworks and platforms such as TensorFlow Serving, TorchServe, Triton Inference Server, BentoML, KServe, and Seldon Core, plus container/runtime concerns like Docker images, GPU-enabled containers, base image selection, container entrypoints, runtime dependencies, and image scanning for model services.",
        "slug": "model-serving-deployment-and-runtime-packaging",
        "source": "db"
      },
      "input_skill": "MLflow",
      "llm_role": null,
      "roles_from_db": [
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          "display_name": "MLOps Engineer",
          "id": 5,
          "rationale": null,
          "role_archetype": null,
          "slug": "mlops-engineer",
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        },
        {
          "display_name": "Machine Learning Engineer",
          "id": 10,
          "rationale": null,
          "role_archetype": null,
          "slug": "machine-learning-engineer",
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        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Project Delivery and Coordination",
        "id": 366,
        "rationale": "Coordination practices for organizing work, tracking progress, and aligning stakeholders across a delivery effort. Agile fits here when used as a team execution framework for managing scope, cadence, and collaboration.",
        "slug": "d_init_02",
        "source": "db"
      },
      "input_skill": "MLflow",
      "llm_role": null,
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    },
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        "display_name": "Version Control Systems",
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        "slug": "d_init_01",
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      "input_skill": "MLflow",
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      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Model Serving Deployment and Runtime Packaging",
        "id": 52,
        "rationale": "Operational deployment of trained models into online, batch, or streaming serving environments, including packaging models and model servers into containers or managed inference runtimes, coordinating rollout, and handing off to inference systems. Covers serving frameworks and platforms such as TensorFlow Serving, TorchServe, Triton Inference Server, BentoML, KServe, and Seldon Core, plus container/runtime concerns like Docker images, GPU-enabled containers, base image selection, container entrypoints, runtime dependencies, and image scanning for model services.",
        "slug": "model-serving-deployment-and-runtime-packaging",
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      "input_skill": "Kubeflow",
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          "rationale": null,
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          "display_name": "Machine Learning Engineer",
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      "input_skill": "Kubeflow",
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              "display_name": "Data Analyst",
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          "alias_type": "VERSION",
          "id": 313,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Kubernetes 1.7",
          "alias_type": "VERSION",
          "id": 314,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Kubernetes 1.8",
          "alias_type": "VERSION",
          "id": 315,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Kubernetes 1.9",
          "alias_type": "VERSION",
          "id": 316,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Kubernetes 1.x",
          "alias_type": "VERSION",
          "id": 317,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Kubernetes v1",
          "alias_type": "VERSION",
          "id": 306,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "k8s",
          "alias_type": "VERSION",
          "id": 305,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 13,
        "display_name": "Kubernetes",
        "id": 158,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "kubernetes",
        "sub_category_id": 1524,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Orchestration Platforms",
            "id": 25,
            "rationale": "Operates the platforms that schedule and run containerized workloads and related deployment primitives. This is separate from image delivery because it concerns runtime placement and service rollout behavior.",
            "slug": "orchestration-platforms",
            "source": "db"
          },
          "input_skill": "Kubernetes",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Engineer",
              "id": 18,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-engineer",
              "source": "db"
            },
            {
              "display_name": "DevOps Engineer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
              "slug": "devops-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Kubernetes",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "SQL",
          "alias_type": "CANONICAL",
          "id": 3398,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 5,
        "display_name": "SQL",
        "id": 2601,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "sql",
        "sub_category_id": 55,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Relational Data Modeling",
            "id": 71,
            "rationale": "Designing tables, relationships, constraints, and transactional data shapes for operational backend systems. This cluster is coherent because backend services frequently own the canonical application data model.",
            "slug": "relational-data-modeling",
            "source": "db"
          },
          "input_skill": "SQL",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Backend Engineer",
              "id": 14,
              "rationale": null,
              "role_archetype": null,
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "Data Engineer",
              "id": 6,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Version Control Systems",
            "id": 365,
            "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "SQL",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "SQL",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "NoSQL",
          "alias_type": "CANONICAL",
          "id": 3592,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "NoSQL",
        "id": 2639,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "nosql",
        "sub_category_id": 2150,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Data Access and Query Optimization",
            "id": 74,
            "rationale": "Techniques for making analytical data fast and reliable to query. This includes partitioning, clustering, indexing choices, file layout, and access-path tuning for downstream consumers.",
            "slug": "data-access-and-query-optimization",
            "source": "db"
          },
          "input_skill": "NoSQL",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 6,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "NoSQL",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "AWS",
          "alias_type": "CANONICAL",
          "id": 348,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 13,
        "display_name": "AWS",
        "id": 163,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "aws",
        "sub_category_id": 161,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cloud Platform Operations",
            "id": 26,
            "rationale": "Uses cloud provider services to support delivery and runtime environments. The focus is on consumer-level operation of cloud services rather than deep cloud architecture ownership.",
            "slug": "cloud-platform-operations",
            "source": "db"
          },
          "input_skill": "AWS",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "DevOps Engineer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
              "slug": "devops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cloud Security Platforms",
            "id": 332,
            "rationale": "Cloud-native security products used to assess posture, detect misconfigurations, and monitor workloads across AWS, Azure, and GCP. This is a distinct product family because the role often works across multiple CNAPP/CSPM/CWPP offerings and cloud-native detectors.",
            "slug": "cloud-security-platforms",
            "source": "db"
          },
          "input_skill": "AWS",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cybersecurity Engineer",
              "id": 9,
              "rationale": null,
              "role_archetype": null,
              "slug": "cybersecurity-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "AWS",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "GCP",
          "alias_type": "CANONICAL",
          "id": 3043,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 13,
        "display_name": "GCP",
        "id": 2304,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "gcp",
        "sub_category_id": 161,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cloud Security Platforms",
            "id": 332,
            "rationale": "Cloud-native security products used to assess posture, detect misconfigurations, and monitor workloads across AWS, Azure, and GCP. This is a distinct product family because the role often works across multiple CNAPP/CSPM/CWPP offerings and cloud-native detectors.",
            "slug": "cloud-security-platforms",
            "source": "db"
          },
          "input_skill": "GCP",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cybersecurity Engineer",
              "id": 9,
              "rationale": null,
              "role_archetype": null,
              "slug": "cybersecurity-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "GCP",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Git",
          "alias_type": "CANONICAL",
          "id": 3375,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 11,
        "display_name": "Git",
        "id": 2578,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "git",
        "sub_category_id": 2101,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Version Control Systems",
            "id": 365,
            "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "Git",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Git",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "CI/CD",
          "alias_type": "CANONICAL",
          "id": 3376,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 7,
        "display_name": "CI/CD",
        "id": 2579,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "ci-cd",
        "sub_category_id": 2102,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Version Control Systems",
            "id": 365,
            "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "CI/CD",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "CI/CD",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "MLflow",
          "alias_type": "CANONICAL",
          "id": 3593,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 11,
        "display_name": "MLflow",
        "id": 2640,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "TOOL",
        "slug": "mlflow",
        "sub_category_id": 2151,
        "typical_lifespan": "EVERGREEN",
        "volatility": "EMERGING"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Model Serving Deployment and Runtime Packaging",
            "id": 52,
            "rationale": "Operational deployment of trained models into online, batch, or streaming serving environments, including packaging models and model servers into containers or managed inference runtimes, coordinating rollout, and handing off to inference systems. Covers serving frameworks and platforms such as TensorFlow Serving, TorchServe, Triton Inference Server, BentoML, KServe, and Seldon Core, plus container/runtime concerns like Docker images, GPU-enabled containers, base image selection, container entrypoints, runtime dependencies, and image scanning for model services.",
            "slug": "model-serving-deployment-and-runtime-packaging",
            "source": "db"
          },
          "input_skill": "MLflow",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "MLOps Engineer",
              "id": 5,
              "rationale": null,
              "role_archetype": null,
              "slug": "mlops-engineer",
              "source": "db"
            },
            {
              "display_name": "Machine Learning Engineer",
              "id": 10,
              "rationale": null,
              "role_archetype": null,
              "slug": "machine-learning-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Project Delivery and Coordination",
            "id": 366,
            "rationale": "Coordination practices for organizing work, tracking progress, and aligning stakeholders across a delivery effort. Agile fits here when used as a team execution framework for managing scope, cadence, and collaboration.",
            "slug": "d_init_02",
            "source": "db"
          },
          "input_skill": "MLflow",
          "llm_role": null,
          "roles_from_db": []
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Version Control Systems",
            "id": 365,
            "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "MLflow",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "MLflow",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Kubeflow",
          "alias_type": "CANONICAL",
          "id": 3594,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Kubeflow 1.x",
          "alias_type": "VERSION",
          "id": 3595,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Kubeflow 2.x",
          "alias_type": "VERSION",
          "id": 3596,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Kubeflow v1",
          "alias_type": "VERSION",
          "id": 3597,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Kubeflow v2",
          "alias_type": "VERSION",
          "id": 3598,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 4,
        "display_name": "Kubeflow",
        "id": 2641,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "FRAMEWORK",
        "slug": "kubeflow",
        "sub_category_id": 2152,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Model Serving Deployment and Runtime Packaging",
            "id": 52,
            "rationale": "Operational deployment of trained models into online, batch, or streaming serving environments, including packaging models and model servers into containers or managed inference runtimes, coordinating rollout, and handing off to inference systems. Covers serving frameworks and platforms such as TensorFlow Serving, TorchServe, Triton Inference Server, BentoML, KServe, and Seldon Core, plus container/runtime concerns like Docker images, GPU-enabled containers, base image selection, container entrypoints, runtime dependencies, and image scanning for model services.",
            "slug": "model-serving-deployment-and-runtime-packaging",
            "source": "db"
          },
          "input_skill": "Kubeflow",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "MLOps Engineer",
              "id": 5,
              "rationale": null,
              "role_archetype": null,
              "slug": "mlops-engineer",
              "source": "db"
            },
            {
              "display_name": "Machine Learning Engineer",
              "id": 10,
              "rationale": null,
              "role_archetype": null,
              "slug": "machine-learning-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Version Control Systems",
            "id": 365,
            "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "Kubeflow",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Kubeflow",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": []
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Scientist",
    "id": 7,
    "rationale": "The primary skill of Python aligns well with the role of a Data Scientist, which also utilizes additional skills in machine learning frameworks, making it a suitable match.",
    "role_archetype": null,
    "slug": "data-scientist",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "TensorFlow",
      "tag": "in_db"
    },
    {
      "skill": "PyTorch",
      "tag": "in_db"
    },
    {
      "skill": "Scikit-learn",
      "tag": "in_db"
    },
    {
      "skill": "REST",
      "tag": "in_db"
    },
    {
      "skill": "Docker",
      "tag": "in_db"
    },
    {
      "skill": "Kubernetes",
      "tag": "in_db"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "NoSQL",
      "tag": "in_db"
    },
    {
      "skill": "AWS",
      "tag": "in_db"
    },
    {
      "skill": "GCP",
      "tag": "in_db"
    },
    {
      "skill": "Git",
      "tag": "in_db"
    },
    {
      "skill": "CI/CD",
      "tag": "in_db"
    },
    {
      "skill": "MLflow",
      "tag": "in_db"
    },
    {
      "skill": "Kubeflow",
      "tag": "in_db"
    }
  ],
  "persistence": {
    "items": [
      {
        "chosen_role_id": 7,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Analytical Programming Languages",
          "id": 82,
          "rationale": "Languages used to clean, transform, analyze, and prototype models in notebooks and scripts. This is the core coding surface for expressing statistical logic and data manipulation in a reproducible way.",
          "slug": "analytical-programming-languages",
          "source": "db"
        },
        "dimension_id": 82,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Analyst",
            "id": 20,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-analyst",
            "source": "db"
          },
          {
            "display_name": "Data Scientist",
            "id": 7,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-scientist",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 7,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Automation Scripting and CLI",
          "id": 48,
          "rationale": "Uses scripts and command-line tooling to execute repeatable Azure operations and reduce manual work. This is a practical cluster because the role frequently automates provisioning, checks, and remediation tasks.",
          "slug": "automation-scripting-and-cli",
          "source": "db"
        },
        "dimension_id": 48,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Azure Cloud Engineer",
            "id": 4,
            "rationale": null,
            "role_archetype": null,
            "slug": "azure-cloud-engineer",
            "source": "db"
          },
          {
            "display_name": "Cloud Engineer",
            "id": 18,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 7,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Automation and Scripting for Operations",
          "id": 361,
          "rationale": "Scripts and lightweight automation used to execute repetitive virtualization tasks and enforce operational consistency. This is the practical glue that reduces manual host and VM administration.",
          "slug": "automation-and-scripting-for-operations",
          "source": "db"
        },
        "dimension_id": 361,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Virtualization Engineer",
            "id": 26,
            "rationale": null,
            "role_archetype": null,
            "slug": "virtualization-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 7,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Network Automation and Scripting",
          "id": 285,
          "rationale": "Covers scripts and automation used to configure, validate, and audit network devices and services. This cluster is coherent because repeatable network operations increasingly depend on programmatic changes and checks.",
          "slug": "network-automation-and-scripting",
          "source": "db"
        },
        "dimension_id": 285,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Network Engineer",
            "id": 21,
            "rationale": null,
            "role_archetype": null,
            "slug": "network-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 7,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for AI Workflows",
          "id": 261,
          "rationale": "Languages used to implement AI feature logic, orchestration, and response handling inside product code. This is the core coding surface for turning prompts and model calls into reliable application behavior.",
          "slug": "programming-languages-for-ai-workflows",
          "source": "db"
        },
        "dimension_id": 261,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "AI Engineer",
            "id": 12,
            "rationale": null,
            "role_archetype": null,
            "slug": "ai-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 7,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Backend Systems",
          "id": 140,
          "rationale": "Languages used to implement server-side business logic, request handlers, workers, and service integrations. This is the core coding surface for backend feature delivery and maintenance.",
          "slug": "programming-languages-for-backend-systems",
          "source": "db"
        },
        "dimension_id": 140,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Backend Engineer",
            "id": 14,
            "rationale": null,
            "role_archetype": null,
            "slug": "backend-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 7,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Data Work",
          "id": 67,
          "rationale": "Languages used to implement data pipelines, transformations, and operational utilities. This is the code layer for expressing extraction, parsing, validation, and orchestration logic in data engineering workflows.",
          "slug": "programming-languages-for-data-work",
          "source": "db"
        },
        "dimension_id": 67,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 6,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 7,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for ML Systems",
          "id": 113,
          "rationale": "Languages used to implement model integration code, inference services, and feature-processing logic. This is the core coding surface for turning trained models into product-facing software components.",
          "slug": "programming-languages-for-ml-systems",
          "source": "db"
        },
        "dimension_id": 113,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Machine Learning Engineer",
            "id": 10,
            "rationale": null,
            "role_archetype": null,
            "slug": "machine-learning-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 7,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Security Work",
          "id": 328,
          "rationale": "Languages used to automate security tasks, write detection logic, and build analysis or remediation tooling. This is the core coding surface for a cybersecurity engineer across scripts, queries, and small utilities.",
          "slug": "programming-languages-for-security-work",
          "source": "db"
        },
        "dimension_id": 328,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cybersecurity Engineer",
            "id": 9,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 7,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Test Automation",
          "id": 193,
          "rationale": "Languages used to implement automated checks, helper utilities, and test harness code. This is the core coding surface for turning test ideas into maintainable automation.",
          "slug": "programming-languages-for-test-automation",
          "source": "db"
        },
        "dimension_id": 193,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Automation Tester",
            "id": 16,
            "rationale": null,
            "role_archetype": null,
            "slug": "automation-tester",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 7,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Security Automation and Scripting",
          "id": 258,
          "rationale": "Automating repeatable security checks, enrichment, and remediation workflows. This cluster is coherent because the role often needs lightweight automation to scale analysis and response.",
          "slug": "security-automation-and-scripting",
          "source": "db"
        },
        "dimension_id": 258,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cybersecurity Engineer",
            "id": 9,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 393,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 7,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Applied Machine Learning Toolkits",
          "id": 94,
          "rationale": "Libraries and frameworks used to prototype and compare models quickly. This dimension captures the concrete tooling layer beneath modeling methods and evaluation.",
          "slug": "applied-machine-learning-toolkits",
          "source": "db"
        },
        "dimension_id": 94,
        "input_skill": "TensorFlow",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Scientist",
            "id": 7,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-scientist",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 558,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 7,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Applied Machine Learning Toolkits",
          "id": 94,
          "rationale": "Libraries and frameworks used to prototype and compare models quickly. This dimension captures the concrete tooling layer beneath modeling methods and evaluation.",
          "slug": "applied-machine-learning-toolkits",
          "source": "db"
        },
        "dimension_id": 94,
        "input_skill": "PyTorch",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Scientist",
            "id": 7,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-scientist",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 557,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 7,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Applied Machine Learning Toolkits",
          "id": 94,
          "rationale": "Libraries and frameworks used to prototype and compare models quickly. This dimension captures the concrete tooling layer beneath modeling methods and evaluation.",
          "slug": "applied-machine-learning-toolkits",
          "source": "db"
        },
        "dimension_id": 94,
        "input_skill": "Scikit-learn",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "Data Scientist",
            "id": 7,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-scientist",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 554,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 7,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "API Integration and Data Fetching",
          "id": 9,
          "rationale": "Connecting frontend applications to backend services and third-party endpoints. This covers request orchestration, error handling, pagination, and shaping remote data for UI consumption.",
          "slug": "api-integration-and-data-fetching",
          "source": "db"
        },
        "dimension_id": 9,
        "input_skill": "REST",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Frontend Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": "Frontend Engineers design and build the user-facing parts of applications, translating product and design requirements into interactive experiences. They focus on how the application looks, behaves, and responds in the browser, ensuring usability, accessibility, and consistency across the interface.",
            "slug": "frontend-engineer",
            "source": "db"
          },
          {
            "display_name": "Full Stack Developer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "full-stack-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 121,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 7,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Containerization and Image Delivery",
          "id": 24,
          "rationale": "Builds, packages, and ships application and support workloads as container images. This cluster covers the artifact format and the mechanics of producing deployable images.",
          "slug": "containerization-and-image-delivery",
          "source": "db"
        },
        "dimension_id": 24,
        "input_skill": "Docker",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "DevOps Engineer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
            "slug": "devops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 153,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 7,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Model Serving Deployment and Runtime Packaging",
          "id": 52,
          "rationale": "Operational deployment of trained models into online, batch, or streaming serving environments, including packaging models and model servers into containers or managed inference runtimes, coordinating rollout, and handing off to inference systems. Covers serving frameworks and platforms such as TensorFlow Serving, TorchServe, Triton Inference Server, BentoML, KServe, and Seldon Core, plus container/runtime concerns like Docker images, GPU-enabled containers, base image selection, container entrypoints, runtime dependencies, and image scanning for model services.",
          "slug": "model-serving-deployment-and-runtime-packaging",
          "source": "db"
        },
        "dimension_id": 52,
        "input_skill": "Docker",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "MLOps Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "mlops-engineer",
            "source": "db"
          },
          {
            "display_name": "Machine Learning Engineer",
            "id": 10,
            "rationale": null,
            "role_archetype": null,
            "slug": "machine-learning-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 153,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 7,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Orchestration Platforms",
          "id": 25,
          "rationale": "Operates the platforms that schedule and run containerized workloads and related deployment primitives. This is separate from image delivery because it concerns runtime placement and service rollout behavior.",
          "slug": "orchestration-platforms",
          "source": "db"
        },
        "dimension_id": 25,
        "input_skill": "Kubernetes",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Engineer",
            "id": 18,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-engineer",
            "source": "db"
          },
          {
            "display_name": "DevOps Engineer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
            "slug": "devops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 158,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 7,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Relational Data Modeling",
          "id": 71,
          "rationale": "Designing tables, relationships, constraints, and transactional data shapes for operational backend systems. This cluster is coherent because backend services frequently own the canonical application data model.",
          "slug": "relational-data-modeling",
          "source": "db"
        },
        "dimension_id": 71,
        "input_skill": "SQL",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Backend Engineer",
            "id": 14,
            "rationale": null,
            "role_archetype": null,
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Data Engineer",
            "id": 6,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2601,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 7,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Version Control Systems",
          "id": 365,
          "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 365,
        "input_skill": "SQL",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [],
        "skill_dimension_saved": true,
        "skill_id": 2601,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 7,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Data Access and Query Optimization",
          "id": 74,
          "rationale": "Techniques for making analytical data fast and reliable to query. This includes partitioning, clustering, indexing choices, file layout, and access-path tuning for downstream consumers.",
          "slug": "data-access-and-query-optimization",
          "source": "db"
        },
        "dimension_id": 74,
        "input_skill": "NoSQL",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 6,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2639,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 7,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Platform Operations",
          "id": 26,
          "rationale": "Uses cloud provider services to support delivery and runtime environments. The focus is on consumer-level operation of cloud services rather than deep cloud architecture ownership.",
          "slug": "cloud-platform-operations",
          "source": "db"
        },
        "dimension_id": 26,
        "input_skill": "AWS",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "DevOps Engineer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A DevOps Engineer enables reliable, repeatable delivery of software by designing and operating the processes that connect development and production. They focus on improving deployment flow, operational stability, and collaboration between teams through automation, standardization, and monitoring of delivery and runtime practices.",
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}

LLM Calls

Every model call made for this run, in pipeline order. Click a card to see the model's response.

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