Pipeline run
03d67674-302b-4021-823d-f10b7313a39d
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
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.
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.
Aliases — catalog
- Cobalt Strike (CANONICAL) primary
Context tags (catalog)
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) |
Aliases — catalog
- shader graphs (CANONICAL) primary
Context tags (catalog)
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 |
Aliases — catalog
- GLSL (CANONICAL) primary
Context tags (catalog)
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 |
Aliases — catalog
- post-processing (CANONICAL) primary
Context tags (catalog)
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 |
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)
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) |
Aliases — catalog
- Metabase (CANONICAL) primary
Context tags (catalog)
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) |
Aliases — catalog
- Column-level security (CANONICAL) primary
Context tags (catalog)
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) |
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) |
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) |
Aliases — catalog
- Compaction (CANONICAL) primary
Context tags (catalog)
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) |
Aliases — catalog
- ASGI (CANONICAL) primary
Context tags (catalog)
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) |
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) |
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) |
Aliases — catalog
- effects (CANONICAL) primary
Context tags (catalog)
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) |
Aliases — catalog
- Reactive Forms (CANONICAL) primary
Context tags (catalog)
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)
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Python"
},
{
"is_primary": false,
"skill_name": "TensorFlow"
},
{
"is_primary": false,
"skill_name": "PyTorch"
},
{
"is_primary": false,
"skill_name": "Scikit-learn"
},
{
"is_primary": false,
"skill_name": "REST"
},
{
"is_primary": false,
"skill_name": "Docker"
},
{
"is_primary": false,
"skill_name": "Kubernetes"
},
{
"is_primary": false,
"skill_name": "SQL"
},
{
"is_primary": false,
"skill_name": "NoSQL"
},
{
"is_primary": false,
"skill_name": "AWS"
},
{
"is_primary": false,
"skill_name": "GCP"
},
{
"is_primary": false,
"skill_name": "Git"
},
{
"is_primary": false,
"skill_name": "CI/CD"
},
{
"is_primary": false,
"skill_name": "MLflow"
},
{
"is_primary": false,
"skill_name": "Kubeflow"
}
],
"run_id": null
}
API 2 — extract-details
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"alias_persisted": false,
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "python",
"sub_category_id": 54,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
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{
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"alias_persisted": false,
"existing_alias_id": 862,
"existing_alias_text": "TensorFlow",
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"is_extractable": true,
"skill_nature": "LIBRARY",
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"matched_via": "alias"
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{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 861,
"existing_alias_text": "PyTorch",
"input_term": "PyTorch",
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"is_extractable": true,
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"typical_lifespan": "EVERGREEN",
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{
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"alias_persisted": false,
"existing_alias_id": 852,
"existing_alias_text": "scikit-learn",
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"slug": "scikit-learn",
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"volatility": "STABLE"
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"matched_via": "alias"
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{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
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"existing_alias_text": "REST",
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{
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{
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"is_extractable": true,
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{
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{
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},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
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"skill_nature": "PLATFORM",
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"typical_lifespan": "EVERGREEN",
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},
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{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
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{
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"existing_alias_text": "Git",
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},
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{
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{
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{
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{
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{
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{
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{
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},
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},
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"dimension": {
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},
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"source": "db"
},
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},
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"dimension": {
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"source": "db"
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},
{
"dimension": {
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{
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"source": "db"
},
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},
"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": {
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"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.",
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"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",
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"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,
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"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",
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"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",
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}
],
"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",
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},
{
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"dimension": {
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"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",
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"rationale": null,
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"slug": "devops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
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"skill_tag": "in_db",
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},
{
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"dimension": {
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"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,
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"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "MLOps Engineer",
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"slug": "mlops-engineer",
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},
{
"display_name": "Machine Learning Engineer",
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}
],
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"skill_tag": "in_db",
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},
{
"chosen_role_id": 7,
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"difficulty_hint": "well_known",
"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"
},
"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",
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"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": {
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"display_name": "Relational Data Modeling",
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"source": "db"
},
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"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",
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"source": "db"
},
{
"display_name": "Data Engineer",
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"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
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"skill_id": 2601,
"skill_tag": "in_db",
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},
{
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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"
},
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"input_skill": "SQL",
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"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,
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{
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"source": "db"
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"input_skill": "NoSQL",
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"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": [
{
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],
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"skill_id": 2639,
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},
{
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"matched_chosen_role": false,
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"roles_from_db": [
{
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],
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"skill_id": 163,
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},
{
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"display_name": "Cloud Security Platforms",
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"source": "db"
},
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"input_skill": "AWS",
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"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",
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"slug": "cybersecurity-engineer",
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],
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"skill_id": 163,
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},
{
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"display_name": "Cloud Security Platforms",
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"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.",
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"source": "db"
},
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"input_skill": "GCP",
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"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": [
{
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],
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"skill_tag": "in_db",
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},
{
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"dimension": {
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"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.",
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"source": "db"
},
"dimension_id": 365,
"input_skill": "Git",
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"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": 2578,
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},
{
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},
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"input_skill": "CI/CD",
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"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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"roles_from_db": [],
"skill_dimension_saved": true,
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{
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"slug": "model-serving-deployment-and-runtime-packaging",
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},
"dimension_id": 52,
"input_skill": "MLflow",
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"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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"roles_from_db": [
{
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{
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],
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{
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},
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"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
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"roles_from_db": [],
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{
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},
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},
"dimension_id": 52,
"input_skill": "Kubeflow",
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"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",
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{
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}
],
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"skill_id": 2641,
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},
{
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"display_name": "Version Control Systems",
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"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.",
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"source": "db"
},
"dimension_id": 365,
"input_skill": "Kubeflow",
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"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": 2641,
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}
],
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"planner_output": null,
"run_id": "03d67674-302b-4021-823d-f10b7313a39d"
}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.