Pipeline run
fa7f45ee-ec59-418f-bfa9-a6c75709fdc1
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
MLOps Engineer
domain · AI / ML CASE DOMAINslug: ml-ops-engineer · id: 16 · source: db
Domain=AI / ML; The JD centers on deployment automation, CI/CD, monitoring, infrastructure for ML workloads, and model operations, which closely matches MLOps Engineer.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
We are seeking a talented and experienced MLOps engineer to join our dynamic team. As an MLOps engineer, you will be the architect of seamless integration between machine learning models and production systems. Your role is pivotal in ensuring the reliability, scalability, and efficiency of our machine-learning infrastructure • Suitable person has hand-on experience with a large scale Enterprise Data Lake (EDL) setup in AWS Cloud using Serverless and standard AWS Data Lake Architecture • Should have dealt with EDL connectors for SFDC, Snowflake, Oracle and other third party systems • Need to be familiar with Job monitoring, diagnostics, issue resolution, scalability& performance tuning • Should be able to suggest and design resilient and scalable pipelines for data ingestion and ML Training models • Should be well versed in managing Production grade EDL deployments and best practices related to data access, PII data protection, Access& Control, Data Lifecycle management • 6-8 years’ experience as a Data Architect with experience in managing full lifecycle of Data Engineering and Cloud best practices for Enterprise grade deployments • Good communication skills and proactive problem solver. • Individual contributor role with some mentoring abilities • Knowledge of Databricks or other tools good to have • ITIL and Managed Services Support Model concepts Responsibilities • **Deployment and Automation:** • Design and implement automated workflows for model deployment and monitoring. • Establish CI/CD pipelines for end-to-end automation of model updates and releases. • **Infrastructure Management:** • Collaborate with DevOps and IT teams to optimize infrastructure for machine learning workloads. • Implement scalable solutions for data storage, processing, and model serving. • Develop and optimize workflows for model training, feature engineering, data ingestion, and preprocessing to enable efficient and reproducible machine learning processes. • **Monitoring and Maintenance:** • Develop and maintain monitoring systems to track model performance and system health. • Implement proactive measures for identifying and resolving potential issues. • Work closely with cross-functional teams to identify and resolve operational challenges related to data pipelines, infrastructure, and performance tuning. • **Collaboration:** • Work closely with data scientists, software engineers, and other stakeholders to understand model requirements and integration points. • Provide guidance on best practices for code quality, version control, and deployment strategies to ensure the stability and maintainability of machine learning systems. • **Security and Compliance:** • Ensure security protocols are in place for protecting sensitive data and models. • Stay updated on industry regulations and compliance standards related to machine learning. Qualifications • Bachelor’s or higher degree in Computer Science, Engineering, or a related field. • Proven experience in deploying and maintaining machine learning models in production. • Strong proficiency in scripting languages (e.g., Python) and experience with relevant frameworks (e.g., TensorFlow, PyTorch). • Solid understanding of cloud platforms (AWS preferred) and containerization technologies (e.g., Docker, Kubernetes). • Familiarity with CI/CD tools and version control systems. Skills • Automation and scripting skills for building scalable MLOps workflows. • Strong problem-solving and troubleshooting abilities. • Excellent collaboration and communication skills. • Knowledge of best practices in model monitoring, logging, and debugging.
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
- CI/CD (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Ci Cd Process
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: CI/CD appears in a large share of software engineering JDs and is a standard requirement across DevOps, platform, and backend roles; major vendors like GitHub, GitLab, and AWS all center product roadmaps on CI/CD pipelines.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 900
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
CI/CD Pipeline Platforms Catalog dimension db id 150
Library dimension (catalog)
Roles linked in library: DevOps Engineer
-
CI/CD for Machine Learning Catalog dimension db id 56
Library dimension (catalog)
Roles linked in library: ML Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
CI/CD for Machine Learning
ci-cd-for-machine-learning
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- DevOps (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Devops Methodology
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: DevOps appears in a large share of software and platform engineering job descriptions, often alongside CI/CD, Kubernetes, and cloud tooling; it is a standard hiring-pipeline keyword rather than a niche specialty.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 922
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
CI/CD Pipeline Platforms Catalog dimension db id 150
Library dimension (catalog)
Roles linked in library: DevOps Engineer
-
Deployment and Release Patterns Catalog dimension db id 140
Library dimension (catalog)
Roles linked in library: Cloud Architect
-
Infrastructure as Code Catalog dimension db id 132
Library dimension (catalog)
Roles linked in library: Cloud Architect, DevOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Deployment and Release Patterns
deployment-and-release-patterns
|
✓ | — | 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) |
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
- Data Engineering 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
- Software Development
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Aliases — catalog
- code quality (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Code Quality
- Confidence
- 0.94
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Code quality is a standard hiring-pipeline expectation in JDs for software engineers and is reinforced by widespread use of linters, code review, and CI quality gates across major repos and platforms.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1246
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
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
- Data Engineering 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
- Data Engineering 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
- 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
- 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
- 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
- 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
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- query tuning (CANONICAL) primary
- Query Tuning (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Query Optimization
- Confidence
- 0.82
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common in DB/analytics job descriptions and vendor docs; PostgreSQL, MySQL, SQL Server, and Oracle all expose EXPLAIN/ANALYZE and tuning guides, showing broad hiring-pipeline demand.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 3067
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Relational Database Design Catalog dimension db id 4
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Relational Database Design
relational-database-design
|
— | — |
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
|
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Security Tools
- 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
- Security Tools
- 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 |
|---|---|---|---|---|---|---|
| CI/CD | in_db |
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| CI/CD | in_db |
CI/CD for Machine Learning
ci-cd-for-machine-learning
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| DevOps | in_db |
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| DevOps | in_db |
Deployment and Release Patterns
deployment-and-release-patterns
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| DevOps | in_db |
Infrastructure as Code
infrastructure-as-code
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| 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) | |
| Code Quality | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Performance Tuning | new |
Relational Database Design
relational-database-design
|
— | — | Skipped — no persistable v3 meta for new skill | skill_not_in_db_v3_proposed |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | Data Pipelines | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Version Control | type=Software Development subtype=general nature=PRACTICE lifespan=EVERGREEN | |
| canonical_skill_proposed | Data Ingestion | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Preprocessing | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Feature Engineering | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Model Serving | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Model Monitoring | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Model Deployment | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Model Training | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Security Protocols | type=Security Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Compliance Standards | type=Security Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| dimension_skill_link_proposed | Performance Tuning ↔ Relational Database Design |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
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}
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"max": 8,
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"word_count": 263
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
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],
"kra_match_roles": [
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{
"kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
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"similarity": 0.6982
},
{
"kra_text": "Monitors production model behavior for data drift, concept drift, and prediction performance degradation using monitoring dashboards and alerting.",
"sentence": "Develop and maintain monitoring systems to track model performance and system health.",
"similarity": 0.6333
},
{
"kra_text": "Builds model serving infrastructure to deploy trained models as real-time prediction APIs or batch inference jobs using TorchServe, TensorFlow Serving, or SageMaker.",
"sentence": "Implement scalable solutions for data storage, processing, and model serving.",
"similarity": 0.5799
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 3,
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"slug": "ml-engineer",
"total_count": null
},
{
"display_name": "MLOps Engineer",
"kra_matches": [
{
"kra_text": "Manages the end-to-end ML model release lifecycle from training job completion through validation gates to production deployment approval.",
"sentence": "Establish CI/CD pipelines for end-to-end automation of model updates and releases.",
"similarity": 0.6342
},
{
"kra_text": "Sets up model monitoring dashboards, data drift detection, prediction performance tracking, and alert routing for production ML systems.",
"sentence": "Develop and maintain monitoring systems to track model performance and system health.",
"similarity": 0.6217
},
{
"kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
"sentence": "Design and implement automated workflows for model deployment and monitoring.",
"similarity": 0.6176
}
],
"matched_count": null,
"matched_skills": null,
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"score": 0.6245,
"slug": "ml-ops-engineer",
"total_count": null
},
{
"display_name": "Data Engineer",
"kra_matches": [
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Work closely with data scientists, software engineers, and other stakeholders to understand model requirements and integration points.",
"similarity": 0.7073
},
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Work closely with cross-functional teams to identify and resolve operational challenges related to data pipelines, infrastructure, and performance tuning.",
"similarity": 0.5873
},
{
"kra_text": "Optimizes pipeline throughput, partitioning strategies, and query performance across cloud data warehouses like Snowflake, BigQuery, or Redshift.",
"sentence": "Implement scalable solutions for data storage, processing, and model serving.",
"similarity": 0.5341
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.6096,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "DevOps Engineer",
"kra_matches": [
{
"kra_text": "Collaborates with development teams to improve build processes, reduce deployment friction, containerize applications, and adopt DevOps best practices.",
"sentence": "Collaborate with DevOps and IT teams to optimize infrastructure for machine learning workloads.",
"similarity": 0.6203
},
{
"kra_text": "Monitors CI/CD pipeline reliability, identifies bottlenecks in delivery workflows, and improves deployment frequency, lead time, and failure recovery rate.",
"sentence": "Establish CI/CD pipelines for end-to-end automation of model updates and releases.",
"similarity": 0.6013
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{
"kra_text": "Monitors CI/CD pipeline reliability, identifies bottlenecks in delivery workflows, and improves deployment frequency, lead time, and failure recovery rate.",
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],
"matched_count": null,
"matched_skills": null,
"role_id": 10,
"score": 0.6068,
"slug": "devops-engineer",
"total_count": null
},
{
"display_name": "Fullstack Developer",
"kra_matches": [
{
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"sentence": "Establish CI/CD pipelines for end-to-end automation of model updates and releases.",
"similarity": 0.6217
},
{
"kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
"sentence": "Work closely with data scientists, software engineers, and other stakeholders to understand model requirements and integration points.",
"similarity": 0.54
},
{
"kra_text": "Delivers features through CI/CD pipelines using automated tests, staged rollouts, feature flags, and incremental deployments.",
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}
],
"matched_count": null,
"matched_skills": null,
"role_id": 15,
"score": 0.5562,
"slug": "full-stack-engineer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "ML Engineer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"CI/CD",
"Machine Learning"
],
"role_id": 3,
"score": 0.4,
"slug": "ml-engineer",
"total_count": 5
},
{
"display_name": "DevOps Engineer",
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"matched_count": 2,
"matched_skills": [
"CI/CD",
"DevOps"
],
"role_id": 10,
"score": 0.4,
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"total_count": 5
},
{
"display_name": "Cloud Architect",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"DevOps"
],
"role_id": 9,
"score": 0.2,
"slug": "cloud-architect",
"total_count": 5
},
{
"display_name": "AI Engineer",
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"matched_count": 1,
"matched_skills": [
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],
"role_id": 13,
"score": 0.2,
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},
{
"display_name": "MLOps Engineer",
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"matched_count": 1,
"matched_skills": [
"Machine Learning"
],
"role_id": 16,
"score": 0.2,
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]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "DOMAIN",
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"matched_count": null,
"matched_skills": null,
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},
"confidence": 0.99,
"is_new_role": false,
"llm2_fired": false,
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"ML workflow engineering",
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"Provide guidance on best practices for code quality, version control, and deployment strategies",
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"matched_skills": [
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"CI/CD pipelines",
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"model serving",
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"version control",
"deployment strategies",
"security protocols"
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"new_role_slug": null,
"queued": false,
"reasoning": "Domain=AI / ML; The JD centers on deployment automation, CI/CD, monitoring, infrastructure for ML workloads, and model operations, which closely matches MLOps Engineer.",
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},
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"centroid_n_after": 9,
"centroid_updated": true,
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"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 19335,
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},
{
"is_primary": true,
"queue_id": 19336,
"role_display_name": "MLOps Engineer",
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},
{
"is_primary": false,
"queue_id": 19337,
"role_display_name": "MLOps Engineer",
"role_slug": "ml-ops-engineer",
"skill_name": "Data Ingestion",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 19338,
"role_display_name": "MLOps Engineer",
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},
{
"is_primary": false,
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"role_display_name": "MLOps Engineer",
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"skill_name": "Feature Engineering",
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},
{
"is_primary": false,
"queue_id": 19340,
"role_display_name": "MLOps Engineer",
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},
{
"is_primary": false,
"queue_id": 19341,
"role_display_name": "MLOps Engineer",
"role_slug": "ml-ops-engineer",
"skill_name": "Model Monitoring",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 19342,
"role_display_name": "MLOps Engineer",
"role_slug": "ml-ops-engineer",
"skill_name": "Model Deployment",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 19343,
"role_display_name": "MLOps Engineer",
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"skill_name": "Model Training",
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},
{
"is_primary": false,
"queue_id": 19344,
"role_display_name": "MLOps Engineer",
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"skill_name": "Performance Tuning",
"status": "pending"
},
{
"is_primary": false,
"queue_id": 19345,
"role_display_name": "MLOps Engineer",
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},
{
"is_primary": false,
"queue_id": 19346,
"role_display_name": "MLOps Engineer",
"role_slug": "ml-ops-engineer",
"skill_name": "Compliance Standards",
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}
],
"queue_entry_id": null,
"v3_pipeline_triggered": false,
"v3_role_slug": null,
"v3_run_id": null
}
}
API 2 — extract-details
{
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"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 1826,
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"matched_canonical": {
"category_id": 8,
"display_name": "CI/CD",
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "ci-cd",
"sub_category_id": 900,
"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": 1852,
"existing_alias_text": "DevOps",
"input_term": "DevOps",
"matched_canonical": {
"category_id": 8,
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
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"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,
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"input_term": "Machine Learning",
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"category_id": 2,
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "machine-learning",
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"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": 2623,
"existing_alias_text": "code quality",
"input_term": "Code Quality",
"matched_canonical": {
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"display_name": "code quality",
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
"alias_persisted": false,
"existing_alias_id": 5584,
"existing_alias_text": "Query Tuning",
"input_term": "Performance Tuning",
"matched_canonical": {
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"is_extractable": true,
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"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "embedding_alias"
}
],
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{
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},
{
"dimension": {
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},
{
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]
},
{
"dimension": {
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},
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]
},
{
"dimension": {
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"input_skill": "DevOps",
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"roles_from_db": [
{
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{
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]
},
{
"dimension": {
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]
},
{
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},
{
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},
{
"dimension": {
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},
"input_skill": "Performance Tuning",
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{
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{
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{
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{
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{
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]
}
],
"input_final_skills": [
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"input_llm_skills": [
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"new_aliases_persisted": 0,
"run_id": "fa7f45ee-ec59-418f-bfa9-a6c75709fdc1",
"skills_detail": [
{
"aliases_in_db": [
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],
"canonical": {
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"dimensions": [
{
"dimension": {
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"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "CI/CD for Machine Learning",
"id": 56,
"rationale": "Tools and platforms for automating ML model integration, testing, and deployment pipelines.",
"slug": "ci-cd-for-machine-learning",
"source": "db"
},
"input_skill": "CI/CD",
"llm_role": null,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "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": "DevOps",
"alias_type": "CANONICAL",
"id": 1852,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 8,
"display_name": "DevOps",
"id": 1216,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "devops",
"sub_category_id": 922,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "CI/CD Pipeline Platforms",
"id": 150,
"rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
"slug": "ci-cd-pipeline-platforms",
"source": "db"
},
"input_skill": "DevOps",
"llm_role": null,
"roles_from_db": [
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Deployment and Release Patterns",
"id": 140,
"rationale": "Patterns for promoting changes safely across environments, including rollout, rollback, and release gating strategies. Cloud Architects define these patterns so teams can deploy consistently across the platform.",
"slug": "deployment-and-release-patterns",
"source": "db"
},
"input_skill": "DevOps",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Infrastructure as Code",
"id": 132,
"rationale": "Declarative provisioning and environment definition tools used to codify cloud infrastructure, repeatable environments, and platform standards. Cloud Architects use these to express reference architectures and guardrails.",
"slug": "infrastructure-as-code",
"source": "db"
},
"input_skill": "DevOps",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
}
]
}
],
"input_skill": "DevOps",
"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": "Machine Learning",
"alias_type": "CANONICAL",
"id": 2015,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"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"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "AI Governance and Model Security",
"id": 50,
"rationale": "Controls and documentation used to make models safer, auditable, and compliant. ML engineers use this to manage model risk, supply chain integrity, and governance requirements.",
"slug": "ai-governance-and-model-security",
"source": "db"
},
"input_skill": "Machine Learning",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"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"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "Machine Learning",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Machine Learning",
"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": "Data Pipelines",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering 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": "data-pipelines",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Version Control",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Software Development",
"skill_nature": "PRACTICE",
"sub_category": "general",
"typical_lifespan": "EVERGREEN",
"version_strategy": "UNVERSIONED",
"volatility": "STABLE"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "version-control",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "code quality",
"alias_type": "CANONICAL",
"id": 2623,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 2,
"display_name": "code quality",
"id": 1653,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "code-quality",
"sub_category_id": 1246,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "Code Quality",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Code Quality",
"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": "Data Ingestion",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering 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": "data-ingestion",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Preprocessing",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering 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": "preprocessing",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Feature Engineering",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Machine Learning Frameworks",
"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": "feature-engineering",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Model Serving",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Machine Learning Frameworks",
"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": "model-serving",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Model Monitoring",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Machine Learning Frameworks",
"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": "model-monitoring",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Model Deployment",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Machine Learning Frameworks",
"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": "model-deployment",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Model Training",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Machine Learning Frameworks",
"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": "model-training",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "query tuning",
"alias_type": "CANONICAL",
"id": 5119,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Query Tuning",
"alias_type": "CANONICAL",
"id": 5584,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 2,
"display_name": "query tuning",
"id": 3553,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "query-tuning",
"sub_category_id": 3067,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Relational Database Design",
"id": 4,
"rationale": "Modeling and operating relational persistence for backend services. Includes schema design, normalization, indexing, transactions, and query tuning for operational data stores.",
"slug": "relational-database-design",
"source": "db"
},
"input_skill": "Performance Tuning",
"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": "Kotlin Backend Developer",
"id": 84,
"rationale": null,
"role_archetype": "Engineering",
"slug": "kotlin-server-backend-developer",
"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": "Ruby Backend Developer",
"id": 85,
"rationale": null,
"role_archetype": "Engineering",
"slug": "ruby-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
]
}
],
"input_skill": "Performance Tuning",
"matched_via": "embedding_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": "Security Protocols",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Security Tools",
"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": "security-protocols",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Compliance Standards",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Security Tools",
"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": "compliance-standards",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"Data Pipelines",
"Version Control",
"Data Ingestion",
"Preprocessing",
"Feature Engineering",
"Model Serving",
"Model Monitoring",
"Model Deployment",
"Model Training",
"Security Protocols",
"Compliance Standards"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "MLOps Engineer",
"id": 16,
"rationale": "Domain=AI / ML; The JD centers on deployment automation, CI/CD, monitoring, infrastructure for ML workloads, and model operations, which closely matches MLOps Engineer.",
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "CI/CD",
"tag": "in_db"
},
{
"skill": "DevOps",
"tag": "in_db"
},
{
"skill": "Machine Learning",
"tag": "in_db"
},
{
"skill": "Data Pipelines",
"tag": "new"
},
{
"skill": "Version Control",
"tag": "new"
},
{
"skill": "Code Quality",
"tag": "in_db"
},
{
"skill": "Data Ingestion",
"tag": "new"
},
{
"skill": "Preprocessing",
"tag": "new"
},
{
"skill": "Feature Engineering",
"tag": "new"
},
{
"skill": "Model Serving",
"tag": "new"
},
{
"skill": "Model Monitoring",
"tag": "new"
},
{
"skill": "Model Deployment",
"tag": "new"
},
{
"skill": "Model Training",
"tag": "new"
},
{
"skill": "Performance Tuning",
"tag": "in_db"
},
{
"skill": "Security Protocols",
"tag": "new"
},
{
"skill": "Compliance Standards",
"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": 16,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "CI/CD Pipeline Platforms",
"id": 150,
"rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
"slug": "ci-cd-pipeline-platforms",
"source": "db"
},
"dimension_id": 150,
"input_skill": "CI/CD",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1190,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 16,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "CI/CD for Machine Learning",
"id": 56,
"rationale": "Tools and platforms for automating ML model integration, testing, and deployment pipelines.",
"slug": "ci-cd-for-machine-learning",
"source": "db"
},
"dimension_id": 56,
"input_skill": "CI/CD",
"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": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 1190,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 16,
"dimension": {
"difficulty_hint": "well_known",
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