Pipeline run
de3885d8-b2bf-466b-8284-ca2638731b4d
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
Captured for admin review
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
ML Engineer
CASE Aslug: ml-engineer · id: 3 · source: db
Exact alias hit on ml-engineer (1.0) — no other alias at this confidence; skill_top ml-engineer 0.80 does not contradict
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Company Overview Arcesium is a global financial technology firm that solves complex data-driven challenges faced by some of the world’s most sophisticated financial institutions. We constantly innovate our platform and capabilities to meet tomorrow’s challenges, anticipate the risks our clients encounter, and design advanced solutions to help our clients achieve transformational business outcomes. Financial technology is a high-growth industry as change and innovation continue to disrupt the status-quo and prompt major transformation. Arcesium is at a particularly interesting time in our own growth as we look to leverage our successfully established market position and expand operations in pursuit of strategic new business opportunities. We value intellectual curiosity, proactive ownership, and collaboration with colleagues, and we empower you to meaningfully contribute from day one and accelerate your professional development. We are looking for an innovative Lead AI/ML engineer to join our cutting-edge generative AI team. Our team develops a state-of-the-art gen-ai platform that creates domain-aware agents for financial operations, serving both internal processes and customer-facing products. We design and build scalable AI architectures and implement intelligent systems that operate within the complex world of finance. As a core part of Arcesium's technology innovation, we integrate our AI solutions into the firm's broader financial technology ecosystem, driving efficiency, accuracy, and insights throughout the platform. What You'll Do • Design, develop, and implement complex AI/ML models and algorithms to solve business challenges • Architect and build scalable AI/ML solutions using cloud technologies, particularly AWS • Gather requirements, validate architecture, and create/review high-level and low-level designs for AI/ML projects • Provide technical consultation on AI/ML technologies and solutions for various projects/products • Create and review architectural decisions, ensuring the integration of AI/ML components with existing systems • Ensure compliance of non-functional attributes (stability, scalability, performance, etc.) of AI/ML products to internal standards • Guide and provide technical training on AI/ML topics, influencing business/technical decisions • Own and execute AI/ML projects independently from an architectural standpoint • Collaborate with cross-functional teams to integrate AI/ML solutions into production environments What You'll Need • Bachelor's/Master’s degree in computer science, Machine Learning, AI/ML • 5+ years of relevant experience in AI/ML, with a strong background in Python, Java, or similar languages • Experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn • Proven track record in designing and implementing end-to-end ML pipelines for enterprise-grade software • Strong knowledge of AI/ML algorithms, statistical modeling, and deep learning techniques • Familiarity with AWS services, especially those related to AI/ML • Experience with Infrastructure as Code tools, particularly Terraform • Familiarity with Gen-AI Agentic frameworks like Langgraph, • Experience with MLOps practices and tools for model deployment and monitoring • Experience building and productionizing Python-based products • Familiarity with SRE principles as they apply to AI/ML systems • Knowledge of AI ethics and responsible AI practices • Experience with natural language processing (NLP) or computer vision projects • Contributions to open-source ML projects or research publications Arcesium and its affiliates do not discriminate in employment matters on the basis of race, color, religion, gender, gender identity, pregnancy, national origin, age, military service eligibility, veteran status, sexual orientation, marital status, disability, or any other category protected by law. Note that for us, this is more than just a legal boilerplate. We are genuinely committed to these principles, which form an important part of our corporate culture, and are eager to hear from extraordinarily well qualified individuals having a wide range of backgrounds and personal characteristics.
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- AWS (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Cloud Platform
- Vendor
- Amazon
- License
- other_open
- Year introduced
- 2006
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: AWS is a hiring-pipeline staple: it appears in a large share of cloud/DevOps job descriptions and dominates public cloud market share, with broad certification and vendor ecosystem support.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 46
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Platforms Catalog dimension db id 20
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Cyber Security Engineer, Data Engineer, DevOps Engineer, Fullstack Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, ML Engineer, MLOps Engineer, Node.js Backend Developer, Python Backend Developer, Scala Backend Developer
-
Cloud Platforms for AI Deployment Catalog dimension db id 211
Library dimension (catalog)
Roles linked in library: AI Engineer
-
Cloud Provider Platforms Catalog dimension db id 131
Library dimension (catalog)
Roles linked in library: Cloud Architect, Cloud Security Engineer
-
Cloud Security Posture Tools Catalog dimension db id 64
Library dimension (catalog)
Roles linked in library: Cloud Security Engineer, Cyber Security Engineer
-
Vendor Product Families Catalog dimension db id 477
Library dimension (catalog)
Roles linked in library: Engineering Manager
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Platforms
cloud-platforms
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cloud Provider Platforms
cloud-provider-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cloud Security Posture Tools
cloud-security-posture-tools
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Vendor Product Families
vendor-product-families
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Python (CANONICAL) primary
- Python 2 (VERSION)
- Python 2.x (VERSION)
- Python 3 (VERSION)
- Python 3.10 (VERSION)
- Python 3.11 (VERSION)
- Python 3.12 (VERSION)
- Python 3.x (VERSION)
- py (VERSION)
- py2 (VERSION)
- py3 (VERSION)
- python 3 (VERSION)
- python 3.x (VERSION)
- python2 (VERSION)
- python3 (VERSION)
- python3.x (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- PSF
- License
- mit
- Year introduced
- 1991
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 3
Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 96
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Security Scripting & DSL Languages Catalog dimension db id 248
Library dimension (catalog)
Roles linked in library: Cloud Security Engineer
-
Programming Languages Catalog dimension db id 1
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer
-
Programming Languages & DSLs Catalog dimension db id 475
Library dimension (catalog)
Roles linked in library: Engineering Manager
-
Programming Languages and Scripting Catalog dimension db id 59
Library dimension (catalog)
Roles linked in library: Cyber Security Engineer
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
-
Programming Languages for ML Systems Catalog dimension db id 39
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
-
Programming Languages for XR Catalog dimension db id 97
Library dimension (catalog)
Roles linked in library: AR/VR Engineer
-
Python Programming Catalog dimension db id 290
Library dimension (catalog)
Roles linked in library: Python Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | 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 saved |
|
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Python Programming
python-programming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Java (CANONICAL) primary
- JDK (VERSION)
- JDK 10 (VERSION)
- JDK 11 (VERSION)
- JDK 12 (VERSION)
- JDK 13 (VERSION)
- JDK 14 (VERSION)
- JDK 15 (VERSION)
- JDK 16 (VERSION)
- JDK 17 (VERSION)
- JDK 18 (VERSION)
- JDK 19 (VERSION)
- JDK 20 (VERSION)
- JDK 21 (VERSION)
- JDK 5 (VERSION)
- JDK 6 (VERSION)
- JDK 7 (VERSION)
- JDK 8 (VERSION)
- JDK 9 (VERSION)
- Java 1.0 (VERSION)
- Java 1.1 (VERSION)
- Java 1.2 (VERSION)
- Java 1.3 (VERSION)
- Java 1.4 (VERSION)
- Java 1.5 (VERSION)
- Java 1.6 (VERSION)
- Java 1.7 (VERSION)
- Java 1.8 (VERSION)
- Java 10 (VERSION)
- Java 11 (VERSION)
- Java 12 (VERSION)
- Java 13 (VERSION)
- Java 14 (VERSION)
- Java 15 (VERSION)
- Java 16 (VERSION)
- Java 17 (VERSION)
- Java 18 (VERSION)
- Java 19 (VERSION)
- Java 20 (VERSION)
- Java 21 (VERSION)
- Java 5 (VERSION)
- Java 6 (VERSION)
- Java 7 (VERSION)
- Java 8 (VERSION)
- Java 9 (VERSION)
- Java11 (VERSION)
- Java17 (VERSION)
- Java21 (VERSION)
- Java8 (VERSION)
- OpenJDK 11 (VERSION)
- OpenJDK 17 (VERSION)
- OpenJDK 21 (VERSION)
- OpenJDK 8 (VERSION)
- java 11 (VERSION)
- java 17 (VERSION)
- java 21 (VERSION)
- java 4 (VERSION)
- java 5 (VERSION)
- java 6 (VERSION)
- java 7 (VERSION)
- java 8 (VERSION)
- java lts (VERSION)
- java-11 (VERSION)
- java-17 (VERSION)
- java-21 (VERSION)
- java-4 (VERSION)
- java-5 (VERSION)
- java-6 (VERSION)
- java-7 (VERSION)
- java-8 (VERSION)
- java11 (VERSION)
- java17 (VERSION)
- java21 (VERSION)
- java4 (VERSION)
- java5 (VERSION)
- java6 (VERSION)
- java7 (VERSION)
- java8 (VERSION)
- jdk 11 (VERSION)
- jdk 17 (VERSION)
- jdk 21 (VERSION)
- jdk 4 (VERSION)
- jdk 5 (VERSION)
- jdk 6 (VERSION)
- jdk 7 (VERSION)
- jdk 8 (VERSION)
- jdk11 (VERSION)
- jdk17 (VERSION)
- jdk21 (VERSION)
- jdk4 (VERSION)
- jdk5 (VERSION)
- jdk6 (VERSION)
- jdk7 (VERSION)
- jdk8 (VERSION)
- jvm21 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- Oracle
- License
- other_open
- Year introduced
- 1995
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 21
Maturity reasoning: Java is a hiring-pipeline staple with very high JD volume across enterprise backend, Android, and cloud roles; it remains widely supported by major vendors and frameworks like Spring.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 96
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Java Language and JVM Catalog dimension db id 279
Library dimension (catalog)
Roles linked in library: Java Backend Developer, Kotlin Backend Developer, Scala Backend Developer
-
Kotlin and Java Catalog dimension db id 161
Library dimension (catalog)
Roles linked in library: Android Developer
-
Native Mobile Languages Catalog dimension db id 274
Library dimension (catalog)
Roles linked in library: Native Mobile Developer
-
Pega Programming Languages & DSLs Catalog dimension db id 267
Library dimension (catalog)
Roles linked in library: Pega Developer
-
Programming Languages Catalog dimension db id 1
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer
-
Programming Languages & DSLs Catalog dimension db id 475
Library dimension (catalog)
Roles linked in library: Engineering Manager
-
Programming Languages for Data Work Catalog dimension db id 21
Library dimension (catalog)
Roles linked in library: Data Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Java Language and JVM
java-language-and-jvm
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Kotlin and Java
kotlin-and-java
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Native Mobile Languages
native-mobile-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | 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) |
Aliases — catalog
- TensorFlow (CANONICAL) primary
- TF1 (VERSION)
- TF2 (VERSION)
- TensorFlow 1 (VERSION)
- TensorFlow 1.x (VERSION)
- TensorFlow 2 (VERSION)
- TensorFlow 2.x (VERSION)
- tensorflow 1 (VERSION)
- tensorflow 1.x (VERSION)
- tensorflow 2 (VERSION)
- tensorflow 2.x (VERSION)
- tensorflow v1 (VERSION)
- tensorflow v2 (VERSION)
- tf (VERSION)
- tf1 (VERSION)
- tf2 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Library
- Sub-category
- Machine Learning Library
- Vendor
- License
- apache_2
- Year introduced
- 2015
- Confidence
- 0.90
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 2.x
Maturity reasoning: TensorFlow appears in many ML/AI job descriptions and remains a standard production framework, with strong GitHub activity and broad vendor support from Google and cloud platforms.
Skill profile (library / DB)
- Skill nature
- LIBRARY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 7
- Sub-category id
- 156
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ML Frameworks and Libraries Catalog dimension db id 40
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
ML Frameworks and Libraries
ml-frameworks-and-libraries
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- PyTorch (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Library
- Sub-category
- Machine Learning Library
- Vendor
- Meta
- License
- bsd
- Year introduced
- 2016
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: PyTorch appears in a large volume of ML/AI job descriptions and is a standard framework in research and production, alongside TensorFlow and CUDA ecosystems.
Skill profile (library / DB)
- Skill nature
- LIBRARY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 7
- Sub-category id
- 156
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ML Frameworks and Libraries Catalog dimension db id 40
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
-
Model Fine-Tuning & Adaptation Catalog dimension db id 212
Library dimension (catalog)
Roles linked in library: AI Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
ML Frameworks and Libraries
ml-frameworks-and-libraries
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
Model Fine-Tuning & Adaptation
model-fine-tuning-adaptation
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- scikit-learn (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Library
- Sub-category
- Machine Learning Library
- Vendor
- scikit-learn developers
- License
- bsd
- Year introduced
- 2007
- Confidence
- 0.95
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Commonly listed in ML/data science job descriptions and widely used in production Python ML stacks; no vendor sunset or replacement signal, and GitHub activity remains strong.
Skill profile (library / DB)
- Skill nature
- LIBRARY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 7
- Sub-category id
- 156
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ML Frameworks and Libraries Catalog dimension db id 40
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
ML Frameworks and Libraries
ml-frameworks-and-libraries
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- Machine Learning (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Machine Learning
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Machine Learning appears in large volumes of job descriptions across data, product, and platform roles, and major cloud vendors (AWS, Google Cloud, Azure) offer dedicated ML services and certifications, indicating broad adoption.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1024
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
AI Governance and Model Security Catalog dimension db id 50
Library dimension (catalog)
Roles linked in library: AI Engineer, ML Engineer, MLOps Engineer
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
AI Governance and Model Security
ai-governance-and-model-security
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Terraform (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Infrastructure As Code Tool
- Vendor
- HashiCorp
- License
- mpl
- Year introduced
- 2014
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Terraform is broadly listed in DevOps/SRE/cloud JDs and remains a standard IaC tool across AWS/Azure/GCP; HashiCorp’s ecosystem and widespread GitHub usage signal strong market adoption.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 191
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Infrastructure & Security Automation Frameworks Catalog dimension db id 249
Library dimension (catalog)
Roles linked in library: Cloud Security Engineer
-
Infrastructure as Code Catalog dimension db id 132
Library dimension (catalog)
Roles linked in library: Cloud Architect, DevOps Engineer
-
Infrastructure as Code for ML Catalog dimension db id 57
Library dimension (catalog)
Roles linked in library: ML Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Infrastructure & Security Automation Frameworks
infrastructure-security-automation-frameworks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Infrastructure as Code
infrastructure-as-code
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Infrastructure as Code for ML
infrastructure-as-code-for-ml
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- LangGraph (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Agent Framework
- Vendor
- LangGraph Team
- License
- mit
- Year introduced
- 2023
- Confidence
- 0.95
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: LangGraph is increasingly appearing in AI/agent job descriptions and sits on top of the fast-growing LangChain ecosystem, but it is not yet a universal hiring staple.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 969
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Agentic Frameworks Catalog dimension db id 200
Library dimension (catalog)
Roles linked in library: AI Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Agentic Frameworks
agentic-frameworks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- MLOps (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Mlops
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: MLOps appears in many job descriptions for ML/platform roles and is a standard practice in major cloud vendor docs (AWS, GCP, Azure) for CI/CD, model monitoring, and deployment.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 906
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
CI/CD for Machine Learning Catalog dimension db id 56
Library dimension (catalog)
Roles linked in library: ML Engineer
-
Data Lineage and Metadata Catalog dimension db id 28
Library dimension (catalog)
Roles linked in library: Data Engineer
-
Deployment Rollouts and Release Control Catalog dimension db id 51
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
CI/CD for Machine Learning
ci-cd-for-machine-learning
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
Data Lineage and Metadata
data-lineage-and-metadata
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Deployment Rollouts and Release Control
deployment-rollouts-and-release-control
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Infrastructure Tools
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
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 |
|---|---|---|---|---|---|---|
| AWS | in_db |
Cloud Platforms
cloud-platforms
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| AWS | in_db |
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| AWS | in_db |
Cloud Provider Platforms
cloud-provider-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| AWS | in_db |
Cloud Security Posture Tools
cloud-security-posture-tools
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| AWS | in_db |
Vendor Product Families
vendor-product-families
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | 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 saved | |
| Python | in_db |
Programming Languages for XR
programming-languages-for-xr
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Python Programming
python-programming
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Java Language and JVM
java-language-and-jvm
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Kotlin and Java
kotlin-and-java
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Native Mobile Languages
native-mobile-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Pega Programming Languages & DSLs
pega-programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Java | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| TensorFlow | in_db |
ML Frameworks and Libraries
ml-frameworks-and-libraries
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| PyTorch | in_db |
ML Frameworks and Libraries
ml-frameworks-and-libraries
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| PyTorch | in_db |
Model Fine-Tuning & Adaptation
model-fine-tuning-adaptation
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| scikit-learn | in_db |
ML Frameworks and Libraries
ml-frameworks-and-libraries
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Machine Learning | in_db |
AI Governance and Model Security
ai-governance-and-model-security
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Machine Learning | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Terraform | in_db |
Infrastructure & Security Automation Frameworks
infrastructure-security-automation-frameworks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Terraform | in_db |
Infrastructure as Code
infrastructure-as-code
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Terraform | in_db |
Infrastructure as Code for ML
infrastructure-as-code-for-ml
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| LangGraph | in_db |
Agentic Frameworks
agentic-frameworks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| MLOps | in_db |
CI/CD for Machine Learning
ci-cd-for-machine-learning
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| MLOps | in_db |
Data Lineage and Metadata
data-lineage-and-metadata
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| MLOps | in_db |
Deployment Rollouts and Release Control
deployment-rollouts-and-release-control
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | AI/ML | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Deep Learning | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | SRE | type=Infrastructure Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Natural Language Processing | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Computer Vision | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=MULTI_YEAR |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Arcesium is a global financial",
"last_5_words": "transformational business outcomes."
},
"text": "Arcesium is a global financial technology firm that solves complex data-driven challenges faced by some of the world\u2019s most sophisticated financial institutions. We constantly innovate our platform and capabilities to meet tomorrow\u2019s challenges, anticipate the risks our clients encounter, and design advanced solutions to help our clients achieve transformational business outcomes.",
"word_count": 64
},
"certifications": [],
"company_name": "Arcesium",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"FinTech",
"Banking"
],
"domain": "Financial Services"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE/BSC/MTECH/ME/MSC - Computer Science / Machine Learning / AI/ML",
"raw": "Bachelor\u0027s/Master\u2019s degree in computer science, Machine Learning, AI/ML",
"requirement": "required"
}
],
"experience": {
"max": null,
"min": 5,
"raw": "5+ years of relevant experience in AI/ML"
},
"job_locations": [],
"role": "Lead AI/ML Engineer",
"role_aliases": [
"AI/ML Engineer",
"Machine Learning Engineer",
"Lead Machine Learning Engineer"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 9,
"heading": "What You\u0027ll Do",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Design, develop, and implement",
"last_5_words": "AI/ML solutions into production environments"
},
"text": "\u2022 Design, develop, and implement complex AI/ML models and algorithms to solve business challenges\n\u2022 Architect and build scalable AI/ML solutions using cloud technologies, particularly AWS\n\u2022 Gather requirements, validate architecture, and create/review high-level and low-level designs for AI/ML projects\n\u2022 Provide technical consultation on AI/ML technologies and solutions for various projects/products\n\u2022 Create and review architectural decisions, ensuring the integration of AI/ML components with existing systems\n\u2022 Ensure compliance of non-functional attributes (stability, scalability, performance, etc.) of AI/ML products to internal standards\n\u2022 Guide and provide technical training on AI/ML topics, influencing business/technical decisions\n\u2022 Own and execute AI/ML projects independently from an architectural standpoint\n\u2022 Collaborate with cross-functional teams to integrate AI/ML solutions into production environments",
"word_count": 134
},
{
"bullet_count": 14,
"heading": "What You\u0027ll Need",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Bachelor\u0027s/Master\u2019s degree in computer",
"last_5_words": "projects or research publications"
},
"text": "\u2022 Bachelor\u0027s/Master\u2019s degree in computer science, Machine Learning, AI/ML\n\u2022 5+ years of relevant experience in AI/ML, with a strong background in Python, Java, or similar languages\n\u2022 Experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn\n\u2022 Proven track record in designing and implementing end-to-end ML pipelines for enterprise-grade software\n\u2022 Strong knowledge of AI/ML algorithms, statistical modeling, and deep learning techniques\n\u2022 Familiarity with AWS services, especially those related to AI/ML\n\u2022 Experience with Infrastructure as Code tools, particularly Terraform\n\u2022 Familiarity with Gen-AI Agentic frameworks like Langgraph,\n\u2022 Experience with MLOps practices and tools for model deployment and monitoring\n\u2022 Experience building and productionizing Python-based products\n\u2022 Familiarity with SRE principles as they apply to AI/ML systems\n\u2022 Knowledge of AI ethics and responsible AI practices\n\u2022 Experience with natural language processing (NLP) or computer vision projects\n\u2022 Contributions to open-source ML projects or research publications",
"word_count": 174
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "AI/ML"
},
{
"is_primary": true,
"skill_name": "AWS"
},
{
"is_primary": true,
"skill_name": "Python"
},
{
"is_primary": false,
"skill_name": "Java"
},
{
"is_primary": true,
"skill_name": "TensorFlow"
},
{
"is_primary": true,
"skill_name": "PyTorch"
},
{
"is_primary": true,
"skill_name": "scikit-learn"
},
{
"is_primary": true,
"skill_name": "Machine Learning"
},
{
"is_primary": true,
"skill_name": "Deep Learning"
},
{
"is_primary": true,
"skill_name": "Terraform"
},
{
"is_primary": false,
"skill_name": "LangGraph"
},
{
"is_primary": true,
"skill_name": "MLOps"
},
{
"is_primary": false,
"skill_name": "SRE"
},
{
"is_primary": false,
"skill_name": "Natural Language Processing"
},
{
"is_primary": false,
"skill_name": "Computer Vision"
}
],
"jd_role": {
"display_name": "Lead AI/ML Engineer",
"rationale": null,
"role_aliases": [
"AI/ML Engineer",
"Machine Learning Engineer",
"Lead Machine Learning Engineer"
],
"role_archetype": "Engineering",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Arcesium is a global financial",
"last_5_words": "transformational business outcomes."
},
"text": "Arcesium is a global financial technology firm that solves complex data-driven challenges faced by some of the world\u2019s most sophisticated financial institutions. We constantly innovate our platform and capabilities to meet tomorrow\u2019s challenges, anticipate the risks our clients encounter, and design advanced solutions to help our clients achieve transformational business outcomes.",
"word_count": 64
},
"certifications": [],
"company_name": "Arcesium",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"FinTech",
"Banking"
],
"domain": "Financial Services"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE/BSC/MTECH/ME/MSC - Computer Science / Machine Learning / AI/ML",
"raw": "Bachelor\u0027s/Master\u2019s degree in computer science, Machine Learning, AI/ML",
"requirement": "required"
}
],
"experience": {
"max": null,
"min": 5,
"raw": "5+ years of relevant experience in AI/ML"
},
"job_locations": [],
"role": "Lead AI/ML Engineer",
"role_aliases": [
"AI/ML Engineer",
"Machine Learning Engineer",
"Lead Machine Learning Engineer"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 9,
"heading": "What You\u0027ll Do",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Design, develop, and implement",
"last_5_words": "AI/ML solutions into production environments"
},
"text": "\u2022 Design, develop, and implement complex AI/ML models and algorithms to solve business challenges\n\u2022 Architect and build scalable AI/ML solutions using cloud technologies, particularly AWS\n\u2022 Gather requirements, validate architecture, and create/review high-level and low-level designs for AI/ML projects\n\u2022 Provide technical consultation on AI/ML technologies and solutions for various projects/products\n\u2022 Create and review architectural decisions, ensuring the integration of AI/ML components with existing systems\n\u2022 Ensure compliance of non-functional attributes (stability, scalability, performance, etc.) of AI/ML products to internal standards\n\u2022 Guide and provide technical training on AI/ML topics, influencing business/technical decisions\n\u2022 Own and execute AI/ML projects independently from an architectural standpoint\n\u2022 Collaborate with cross-functional teams to integrate AI/ML solutions into production environments",
"word_count": 134
},
{
"bullet_count": 14,
"heading": "What You\u0027ll Need",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Bachelor\u0027s/Master\u2019s degree in computer",
"last_5_words": "projects or research publications"
},
"text": "\u2022 Bachelor\u0027s/Master\u2019s degree in computer science, Machine Learning, AI/ML\n\u2022 5+ years of relevant experience in AI/ML, with a strong background in Python, Java, or similar languages\n\u2022 Experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn\n\u2022 Proven track record in designing and implementing end-to-end ML pipelines for enterprise-grade software\n\u2022 Strong knowledge of AI/ML algorithms, statistical modeling, and deep learning techniques\n\u2022 Familiarity with AWS services, especially those related to AI/ML\n\u2022 Experience with Infrastructure as Code tools, particularly Terraform\n\u2022 Familiarity with Gen-AI Agentic frameworks like Langgraph,\n\u2022 Experience with MLOps practices and tools for model deployment and monitoring\n\u2022 Experience building and productionizing Python-based products\n\u2022 Familiarity with SRE principles as they apply to AI/ML systems\n\u2022 Knowledge of AI ethics and responsible AI practices\n\u2022 Experience with natural language processing (NLP) or computer vision projects\n\u2022 Contributions to open-source ML projects or research publications",
"word_count": 174
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "de3885d8-b2bf-466b-8284-ca2638731b4d",
"stage3_signals": {
"alias_found": true,
"alias_match_roles": [
{
"display_name": "ML Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 3,
"score": 1.0,
"slug": "ml-engineer",
"total_count": null
}
],
"kra_match_roles": [
{
"display_name": "ML Engineer",
"kra_matches": [
{
"kra_text": "Designs end-to-end ML training pipelines and model inference workflows using TensorFlow, PyTorch, or scikit-learn on cloud ML platforms.",
"sentence": "Proven track record in designing and implementing end-to-end ML pipelines for enterprise-grade software",
"similarity": 0.6246
},
{
"kra_text": "Translates product requirements into machine learning system specifications including feature definitions, model architecture choices, and success metric definitions.",
"sentence": "Gather requirements, validate architecture, and create/review high-level and low-level designs for AI/ML projects",
"similarity": 0.5919
},
{
"kra_text": "Designs end-to-end ML training pipelines and model inference workflows using TensorFlow, PyTorch, or scikit-learn on cloud ML platforms.",
"sentence": "Design, develop, and implement complex AI/ML models and algorithms to solve business challenges",
"similarity": 0.5699
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 3,
"score": 0.5955,
"slug": "ml-engineer",
"total_count": null
},
{
"display_name": "AI Engineer",
"kra_matches": [
{
"kra_text": "Integrates AI model API responses with application business logic, database writes, event publishing, and downstream service orchestration.",
"sentence": "Design, develop, and implement complex AI/ML models and algorithms to solve business challenges",
"similarity": 0.5792
},
{
"kra_text": "Defines evaluation frameworks, automated test suites, and human feedback loops to measure AI feature quality, accuracy, and consistency.",
"sentence": "Ensure compliance of non-functional attributes (stability, scalability, performance, etc. ) of AI/ML products to internal standards",
"similarity": 0.5544
},
{
"kra_text": "Integrates AI model API responses with application business logic, database writes, event publishing, and downstream service orchestration.",
"sentence": "Collaborate with cross-functional teams to integrate AI/ML solutions into production environments",
"similarity": 0.549
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 13,
"score": 0.5608,
"slug": "ai-engineer",
"total_count": null
},
{
"display_name": "AI Compliance Officer",
"kra_matches": [
{
"kra_text": "Monitors deployed AI systems for compliance policy drift, regulatory changes, and emerging requirements affecting existing AI deployments.",
"sentence": "Ensure compliance of non-functional attributes (stability, scalability, performance, etc. ) of AI/ML products to internal standards",
"similarity": 0.5912
},
{
"kra_text": "Manages AI deployment approval workflows, periodic reassessment calendars, and conditional authorization records for production AI systems.",
"sentence": "Create and review architectural decisions, ensuring the integration of AI/ML components with existing systems",
"similarity": 0.5488
},
{
"kra_text": "Reviews AI use cases and model deployments against applicable regulations, internal ethics policies, and governance guidelines prior to production approval.",
"sentence": "Collaborate with cross-functional teams to integrate AI/ML solutions into production environments",
"similarity": 0.5283
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 12,
"score": 0.5561,
"slug": "ai-compliance-officer",
"total_count": null
},
{
"display_name": "Cloud Architect",
"kra_matches": [
{
"kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
"sentence": "Gather requirements, validate architecture, and create/review high-level and low-level designs for AI/ML projects",
"similarity": 0.5678
},
{
"kra_text": "Conducts architecture reviews, approves technical design documents, and guides engineering teams through cloud migration and modernization projects.",
"sentence": "Create and review architectural decisions, ensuring the integration of AI/ML components with existing systems",
"similarity": 0.5586
},
{
"kra_text": "Defines cloud adoption roadmaps, lift-and-shift vs. refactor migration strategies, and landing zone architectures for workloads moving to AWS, Azure, or GCP.",
"sentence": "Architect and build scalable AI/ML solutions using cloud technologies, particularly AWS",
"similarity": 0.5288
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 9,
"score": 0.5517,
"slug": "cloud-architect",
"total_count": null
},
{
"display_name": "MLOps Engineer",
"kra_matches": [
{
"kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
"sentence": "Proven track record in designing and implementing end-to-end ML pipelines for enterprise-grade software",
"similarity": 0.5733
},
{
"kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
"sentence": "Collaborate with cross-functional teams to integrate AI/ML solutions into production environments",
"similarity": 0.5364
},
{
"kra_text": "Validates model performance benchmarks, data schema contracts, and system integration health before signing off on production release readiness.",
"sentence": "Ensure compliance of non-functional attributes (stability, scalability, performance, etc. ) of AI/ML products to internal standards",
"similarity": 0.5323
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 16,
"score": 0.5473,
"slug": "ml-ops-engineer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "ML Engineer",
"kra_matches": null,
"matched_count": 8,
"matched_skills": [
"AWS",
"MLOps",
"Machine Learning",
"PyTorch",
"Python",
"TensorFlow",
"Terraform",
"scikit-learn"
],
"role_id": 3,
"score": 0.8,
"slug": "ml-engineer",
"total_count": 10
},
{
"display_name": "MLOps Engineer",
"kra_matches": null,
"matched_count": 7,
"matched_skills": [
"AWS",
"MLOps",
"Machine Learning",
"PyTorch",
"Python",
"TensorFlow",
"scikit-learn"
],
"role_id": 16,
"score": 0.7,
"slug": "ml-ops-engineer",
"total_count": 10
},
{
"display_name": "Data Engineer",
"kra_matches": null,
"matched_count": 3,
"matched_skills": [
"AWS",
"MLOps",
"Python"
],
"role_id": 2,
"score": 0.3,
"slug": "data-engineer",
"total_count": 10
},
{
"display_name": "AI Engineer",
"kra_matches": null,
"matched_count": 3,
"matched_skills": [
"AWS",
"Machine Learning",
"PyTorch"
],
"role_id": 13,
"score": 0.3,
"slug": "ai-engineer",
"total_count": 10
},
{
"display_name": "Cloud Security Engineer",
"kra_matches": null,
"matched_count": 3,
"matched_skills": [
"AWS",
"Python",
"Terraform"
],
"role_id": 23,
"score": 0.3,
"slug": "cloud-security-engineer",
"total_count": 10
}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "A",
"chosen_role": {
"display_name": "ML Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 3,
"score": 1.0,
"slug": "ml-engineer",
"total_count": null
},
"confidence": 1.0,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [],
"matched_kras": [],
"matched_skills": [],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Exact alias hit on ml-engineer (1.0) \u2014 no other alias at this confidence; skill_top ml-engineer 0.80 does not contradict",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 26,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 19065,
"role_display_name": "ML Engineer",
"role_slug": "ml-engineer",
"skill_name": "AI/ML",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 19066,
"role_display_name": "ML Engineer",
"role_slug": "ml-engineer",
"skill_name": "Deep Learning",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 19067,
"role_display_name": "ML Engineer",
"role_slug": "ml-engineer",
"skill_name": "SRE",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 19068,
"role_display_name": "ML Engineer",
"role_slug": "ml-engineer",
"skill_name": "Natural Language Processing",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 19069,
"role_display_name": "ML Engineer",
"role_slug": "ml-engineer",
"skill_name": "Computer Vision",
"status": "pending"
}
],
"queue_entry_id": null,
"v3_pipeline_triggered": false,
"v3_role_slug": null,
"v3_run_id": null
}
}
API 2 — extract-details
{
"alias_matches": [
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 406,
"existing_alias_text": "AWS",
"input_term": "AWS",
"matched_canonical": {
"category_id": 9,
"display_name": "AWS",
"id": 187,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "PLATFORM",
"slug": "aws",
"sub_category_id": 46,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 67,
"existing_alias_text": "Python",
"input_term": "Python",
"matched_canonical": {
"category_id": 6,
"display_name": "Python",
"id": 5,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "python",
"sub_category_id": 96,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 1,
"existing_alias_text": "Java",
"input_term": "Java",
"matched_canonical": {
"category_id": 6,
"display_name": "Java",
"id": 1,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "java",
"sub_category_id": 96,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 442,
"existing_alias_text": "TensorFlow",
"input_term": "TensorFlow",
"matched_canonical": {
"category_id": 7,
"display_name": "TensorFlow",
"id": 196,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LIBRARY",
"slug": "tensorflow",
"sub_category_id": 156,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 441,
"existing_alias_text": "PyTorch",
"input_term": "PyTorch",
"matched_canonical": {
"category_id": 7,
"display_name": "PyTorch",
"id": 195,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LIBRARY",
"slug": "pytorch",
"sub_category_id": 156,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 449,
"existing_alias_text": "scikit-learn",
"input_term": "scikit-learn",
"matched_canonical": {
"category_id": 7,
"display_name": "scikit-learn",
"id": 197,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LIBRARY",
"slug": "scikit-learn",
"sub_category_id": 156,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 2015,
"existing_alias_text": "Machine Learning",
"input_term": "Machine Learning",
"matched_canonical": {
"category_id": 2,
"display_name": "Machine Learning",
"id": 1356,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "machine-learning",
"sub_category_id": 1024,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 547,
"existing_alias_text": "Terraform",
"input_term": "Terraform",
"matched_canonical": {
"category_id": 13,
"display_name": "Terraform",
"id": 286,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "terraform",
"sub_category_id": 191,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 1889,
"existing_alias_text": "LangGraph",
"input_term": "LangGraph",
"matched_canonical": {
"category_id": 5,
"display_name": "LangGraph",
"id": 1253,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "langgraph",
"sub_category_id": 969,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 1832,
"existing_alias_text": "MLOps",
"input_term": "MLOps",
"matched_canonical": {
"category_id": 8,
"display_name": "MLOps",
"id": 1196,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "mlops",
"sub_category_id": 906,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
}
],
"candidate_roles": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Go Backend Developer",
"id": 81,
"rationale": null,
"role_archetype": "Engineering",
"slug": "go-backend-developer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
},
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
},
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
},
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 435,
"rationale": null,
"role_archetype": "Engineering",
"slug": "fullstack-developer",
"source": "db"
},
{
"display_name": "AR/VR Engineer",
"id": 8,
"rationale": null,
"role_archetype": null,
"slug": "ar-vr-engineer",
"source": "db"
},
{
"display_name": "Android Developer",
"id": 4,
"rationale": null,
"role_archetype": null,
"slug": "android-engineer",
"source": "db"
},
{
"display_name": "Native Mobile Developer",
"id": 75,
"rationale": null,
"role_archetype": "Engineering",
"slug": "native-mobile-developer",
"source": "db"
},
{
"display_name": "Pega Developer",
"id": 24,
"rationale": null,
"role_archetype": null,
"slug": "pega-developer",
"source": "db"
}
],
"chosen_role": {
"display_name": "ML Engineer",
"id": 3,
"rationale": "Exact alias hit on ml-engineer (1.0) \u2014 no other alias at this confidence; skill_top ml-engineer 0.80 does not contradict",
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms",
"id": 20,
"rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
"slug": "cloud-platforms",
"source": "db"
},
"input_skill": "AWS",
"llm_role": null,
"roles_from_db": [
{
"display_name": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Go Backend Developer",
"id": 81,
"rationale": null,
"role_archetype": "Engineering",
"slug": "go-backend-developer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms for AI Deployment",
"id": 211,
"rationale": "Major cloud services that provide infrastructure and managed services for AI workloads.",
"slug": "cloud-platforms-for-ai-deployment",
"source": "db"
},
"input_skill": "AWS",
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"roles_from_db": [
{
"display_name": "AI Engineer",
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"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Provider Platforms",
"id": 131,
"rationale": "Major cloud platforms and their core service ecosystems used to design target-state architectures, choose deployment boundaries, and evaluate managed capabilities. This is the primary substrate for cloud architecture decisions.",
"slug": "cloud-provider-platforms",
"source": "db"
},
"input_skill": "AWS",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Architect",
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"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "Cloud Security Engineer",
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"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Posture Tools",
"id": 64,
"rationale": "Cloud-native security platforms used to assess misconfiguration, workload exposure, and cloud control coverage. This dimension includes the major CNAPP/CSPM/CWPP vendors and cloud security services the role reviews and tunes.",
"slug": "cloud-security-posture-tools",
"source": "db"
},
"input_skill": "AWS",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Vendor Product Families",
"id": 477,
"rationale": "Coordinate usage, licensing, and architecture decisions for major vendor software and cloud product families.",
"slug": "vendor-product-families",
"source": "db"
},
"input_skill": "AWS",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Scripting \u0026 DSL Languages",
"id": 248,
"rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
"slug": "cloud-security-scripting-dsl-languages",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages",
"id": 1,
"rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
"slug": "programming-languages",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 435,
"rationale": null,
"role_archetype": "Engineering",
"slug": "fullstack-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages \u0026 DSLs",
"id": 475,
"rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
"slug": "programming-languages-dsls",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages and Scripting",
"id": 59,
"rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
"slug": "programming-languages-and-scripting",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 21,
"rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for ML Systems",
"id": 39,
"rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
"slug": "programming-languages-for-ml-systems",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "ML Engineer",
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"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
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"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
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}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for XR",
"id": 97,
"rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
"slug": "programming-languages-for-xr",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AR/VR Engineer",
"id": 8,
"rationale": null,
"role_archetype": null,
"slug": "ar-vr-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Python Programming",
"id": 290,
"rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
"slug": "python-programming",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Java Language and JVM",
"id": 279,
"rationale": "Core Java implementation skills used to build backend service logic, utilities, and internal abstractions. This is the primary coding surface for the role and includes language features plus JVM behavior that affect correctness and maintainability.",
"slug": "java-language-and-jvm",
"source": "db"
},
"input_skill": "Java",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
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"rationale": "Practices for safely promoting models through environments and managing rollback when production behavior changes. This dimension covers release gating, version pinning, and rollout strategies specific to ML systems.",
"slug": "deployment-rollouts-and-release-control",
"source": "db"
},
"input_skill": "MLOps",
"llm_role": null,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
]
}
],
"input_skill": "MLOps",
"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": [],
"canonical": null,
"dimensions": [],
"input_skill": "SRE",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Infrastructure Tools",
"skill_nature": "PRACTICE",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "sre",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Natural Language Processing",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Machine Learning Frameworks",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "natural-language-processing",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Computer Vision",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Machine Learning Frameworks",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "computer-vision",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"AI/ML",
"Deep Learning",
"SRE",
"Natural Language Processing",
"Computer Vision"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "ML Engineer",
"id": 3,
"rationale": "Exact alias hit on ml-engineer (1.0) \u2014 no other alias at this confidence; skill_top ml-engineer 0.80 does not contradict",
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "AI/ML",
"tag": "new"
},
{
"skill": "AWS",
"tag": "in_db"
},
{
"skill": "Python",
"tag": "in_db"
},
{
"skill": "Java",
"tag": "in_db"
},
{
"skill": "TensorFlow",
"tag": "in_db"
},
{
"skill": "PyTorch",
"tag": "in_db"
},
{
"skill": "scikit-learn",
"tag": "in_db"
},
{
"skill": "Machine Learning",
"tag": "in_db"
},
{
"skill": "Deep Learning",
"tag": "new"
},
{
"skill": "Terraform",
"tag": "in_db"
},
{
"skill": "LangGraph",
"tag": "in_db"
},
{
"skill": "MLOps",
"tag": "in_db"
},
{
"skill": "SRE",
"tag": "new"
},
{
"skill": "Natural Language Processing",
"tag": "new"
},
{
"skill": "Computer Vision",
"tag": "new"
}
],
"llm_cost_api1_usd": null,
"llm_cost_api2_usd": null,
"llm_cost_api3_usd": null,
"llm_cost_total_usd": null,
"persistence": {
"items": [
{
"chosen_role_id": 3,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms",
"id": 20,
"rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
"slug": "cloud-platforms",
"source": "db"
},
"dimension_id": 20,
"input_skill": "AWS",
"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": ".NET Backend Developer",
"id": 83,
"rationale": null,
"role_archetype": "Engineering",
"slug": "dotnet-backend-developer",
"source": "db"
},
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Go Backend Developer",
"id": 81,
"rationale": null,
"role_archetype": "Engineering",
"slug": "go-backend-developer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 187,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 3,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms for AI Deployment",
"id": 211,
"rationale": "Major cloud services that provide infrastructure and managed services for AI workloads.",
"slug": "cloud-platforms-for-ai-deployment",
"source": "db"
},
"dimension_id": 211,
"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": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 187,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 3,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Provider Platforms",
"id": 131,
"rationale": "Major cloud platforms and their core service ecosystems used to design target-state architectures, choose deployment boundaries, and evaluate managed capabilities. This is the primary substrate for cloud architecture decisions.",
"slug": "cloud-provider-platforms",
"source": "db"
},
"dimension_id": 131,
"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": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 187,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 3,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Posture Tools",
"id": 64,
"rationale": "Cloud-native security platforms used to assess misconfiguration, workload exposure, and cloud control coverage. This dimension includes the major CNAPP/CSPM/CWPP vendors and cloud security services the role reviews and tunes.",
"slug": "cloud-security-posture-tools",
"source": "db"
},
"dimension_id": 64,
"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": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 187,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 3,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Vendor Product Families",
"id": 477,
"rationale": "Coordinate usage, licensing, and architecture decisions for major vendor software and cloud product families.",
"slug": "vendor-product-families",
"source": "db"
},
"dimension_id": 477,
"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": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 187,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 3,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Scripting \u0026 DSL Languages",
"id": 248,
"rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
"slug": "cloud-security-scripting-dsl-languages",
"source": "db"
},
"dimension_id": 248,
"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": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 3,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages",
"id": 1,
"rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
"slug": "programming-languages",
"source": "db"
},
"dimension_id": 1,
"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 Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 435,
"rationale": null,
"role_archetype": "Engineering",
"slug": "fullstack-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 3,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages \u0026 DSLs",
"id": 475,
"rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
"slug": "programming-languages-dsls",
"source": "db"
},
"dimension_id": 475,
"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": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 3,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages and Scripting",
"id": 59,
"rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
"slug": "programming-languages-and-scripting",
"source": "db"
},
"dimension_id": 59,
"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": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 3,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 21,
"rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"dimension_id": 21,
"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": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 3,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for ML Systems",
"id": 39,
"rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
"slug": "programming-languages-for-ml-systems",
"source": "db"
},
"dimension_id": 39,
"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": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 3,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for XR",
"id": 97,
"rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
"slug": "programming-languages-for-xr",
"source": "db"
},
"dimension_id": 97,
"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": "AR/VR Engineer",
"id": 8,
"rationale": null,
"role_archetype": null,
"slug": "ar-vr-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 3,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Python Programming",
"id": 290,
"rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
"slug": "python-programming",
"source": "db"
},
"dimension_id": 290,
"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": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 3,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Java Language and JVM",
"id": 279,
"rationale": "Core Java implementation skills used to build backend service logic, utilities, and internal abstractions. This is the primary coding surface for the role and includes language features plus JVM behavior that affect correctness and maintainability.",
"slug": "java-language-and-jvm",
"source": "db"
},
"dimension_id": 279,
"input_skill": "Java",
"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": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-backend-developer",
"source": "db"
},
{
"display_name": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 3,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Kotlin and Java",
"id": 161,
"rationale": "Primary implementation languages for Android app features, platform integration, and client-side business logic. Android engineers use these languages to build screens, state flows, service adapters, and device-aware behavior.",
"slug": "kotlin-and-java",
"source": "db"
},
"dimension_id": 161,
"input_skill": "Java",
"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,
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{
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