Pipeline run
f8694a18-bf95-438b-8fd6-f2c6f86ce65a
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
• Apply leading Models to solve business problems • Use models from GPT-* to LLama-* to solve wide range of business problems from sales tech, employee productivity, coding and automation • Build RAG …
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
LLM / GenAI Engineer
domain · AI / ML CASE DOMAINslug: llm-genai-engineer · id: 151 · source: db
Domain=AI / ML; The JD is centered on GPT/LLama models, RAG, and LangChain for business applications, which most closely matches LLM/GenAI engineering.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
About The Role We are looking for exceptional engineers to build systems for our very own employees and our customers. Our mission is to enhance Uber's corporate speed, reliability and worker happiness by building products, integrations and platforms. You will work closely with a highly capable team of engineers, data scientists, designers and product managers to ensure Uber's internal products and platforms are a powerful and innovative business advantage. The uSearch team is a part of BizTech that builds scalable, reliable, near realtime and robust enterprise integrations of a plethora of applications and services we build and use at Uber. What You'll Do • Apply leading Models to solve business problems • Use models from GPT-* to LLama-* to solve wide range of business problems from sales tech, employee productivity, coding and automation • Build RAG frameworks • Build Langchain modules • Model Development and Implementation: • Design, develop, and deploy machine learning models to solve complex problems and improve user experiences, operational efficiency, or system performance. • Utilize a variety of data sources, types, and structures to extract actionable insights through predictive analytics and data mining techniques. • Research and Innovation: • Stay abreast of the latest developments in the field of machine learning and artificial intelligence. Evaluate emerging trends and technologies for potential adoption to maintain and expand competitive advantage. • Lead research initiatives that test new algorithms, evaluate new methodologies, and explore innovative uses of data that can lead to scalable solutions. • Team Leadership and Development: • Lead and mentor a team of machine learning engineers and data scientists. Ensure the continuous professional growth of team members through clear goal-setting, regular feedback, and development opportunities. • Foster a collaborative and inclusive team environment that encourages innovation and iterative learning. • Cross-Functional Collaboration: • Work closely with product management, software engineering, and data engineering teams to integrate machine learning models into larger software systems and product offerings. • Partner with stakeholders across the organization to understand business needs and translate them into technical requirements and actionable machine learning projects. • Project Management: • Oversee the full project lifecycle for multiple machine learning initiatives, from ideation and data collection to model development and deployment. • Manage resources, timelines, and risks effectively, ensuring that projects meet their objectives and are delivered on schedule. • Performance Monitoring and Model Optimization: • Continuously monitor the performance of deployed models, identifying opportunities for improvement and optimization. • Implement robust testing and validation strategies to ensure model accuracy and reliability over time. What You'll Need: • Experience in applying Gen-AI/LLM in production • Education: Bachelor's degree in Computer Science, Engineering, or a closely related field. Advanced degrees (Master's or PhD) in fields that emphasize software engineering or machine learning are preferred. • Professional Experience: At least 7 years of experience in software development with a proven track record in both software engineering and research or applied machine learning projects. • Technical Expertise: • Expertise in programming using languages such as Python, Java, C++, or Scala. • Proficient with modern software engineering tools and methodologies (e.g., version control, CI/CD, agile development practices). • Extensive experience with machine learning libraries and frameworks like TensorFlow, PyTorch, or Keras. • Data Proficiency: • Strong capabilities in handling large datasets, with skills in SQL, NoSQL databases, data modeling, and ETL processes. • Experience designing and implementing systems that collect, manage, and convert raw data into actionable insights through machine learning models. • Machine Learning Application Experience: • Solid foundation in applying machine learning algorithms to real-world problems, optimizing algorithms for scalability and performance. • Hands-on experience in building, scaling, and maintaining production-level machine learning models.
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
- 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 skipped (dimension not under chosen role) |
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Artificial Intelligence (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Artificial Intelligence
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: AI appears in a large and growing share of job descriptions across software, data, and product roles, and major vendors (Microsoft, Google, AWS) have standardized AI offerings, signaling broad market adoption.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1020
- 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
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- TOOL
- 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
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- RAG (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Retrieval Augmented Generation
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: RAG appears in many recent AI/ML job descriptions and vendor docs, but it is still not a universal baseline skill like Python or SQL; market demand is rising fast rather than fully standardized.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 904
- 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) |
Aliases — catalog
- LangChain (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Llm Application Framework
- Vendor
- Harrison Chase
- License
- mit
- Year introduced
- 2022
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: LangChain appears in many recent AI/LLM job postings and is widely used in app prototypes, but it’s still not a universal hiring staple like React or AWS.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 146
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
LLM Operations and Orchestration Catalog dimension db id 49
Library dimension (catalog)
Roles linked in library: AI Engineer, ML Engineer, MLOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
LLM Operations and Orchestration
llm-operations-and-orchestration
|
✓ | — | 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
- 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
- Data Engineering 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
- 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
- Testing 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
- Testing Tools
- Sub-category
- general
- Skill nature
- PRACTICE
- 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 |
|---|---|---|---|---|---|---|
| Machine Learning | in_db |
AI Governance and Model Security
ai-governance-and-model-security
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Machine Learning | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Artificial Intelligence | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| RAG | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| LangChain | in_db |
LLM Operations and Orchestration
llm-operations-and-orchestration
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | GPT | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | LLaMA | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Predictive Analytics | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Data Mining | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Model Deployment | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Testing | type=Testing Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | Validation | type=Testing Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": "Uber",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"Tech Consulting",
"Software Development"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE - Computer Science",
"raw": "Education: Bachelor\u0027s degree in Computer Science, Engineering, or a closely related field.",
"requirement": "required"
},
{
"level": "Master\u0027s",
"qualification": "MTECH/ME/MSC - Software Engineering / Machine Learning",
"raw": "Advanced degrees (Master\u0027s or PhD) in fields that emphasize software engineering or machine learning are preferred.",
"requirement": "preferred"
}
],
"experience": {
"max": null,
"min": 7,
"raw": "At least 7 years of experience in software development"
},
"job_locations": [],
"role": "Machine Learning Engineer",
"role_aliases": [
"ML Engineer",
"Machine Learning Developer",
"Data Scientist"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 12,
"heading": "What You\u0027ll Do",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Apply leading Models to solve",
"last_5_words": "accuracy and reliability over time."
},
"text": "\u2022 Apply leading Models to solve business problems\n\u2022 Use models from GPT-* to LLama-* to solve wide range of business problems from sales tech, employee productivity, coding and automation\n\u2022 Build RAG frameworks \n\u2022 Build Langchain modules\n\u2022 Model Development and Implementation:\n\u2022 Design, develop, and deploy machine learning models to solve complex problems and improve user experiences, operational efficiency, or system performance. \n\u2022 Utilize a variety of data sources, types, and structures to extract actionable insights through predictive analytics and data mining techniques. \n\u2022 Research and Innovation:\n\u2022 Stay abreast of the latest developments in the field of machine learning and artificial intelligence. Evaluate emerging trends and technologies for potential adoption to maintain and expand competitive advantage. \n\u2022 Lead research initiatives that test new algorithms, evaluate new methodologies, and explore innovative uses of data that can lead to scalable solutions. \n\u2022 Team Leadership and Development:\n\u2022 Lead and mentor a team of machine learning engineers and data scientists. Ensure the continuous professional growth of team members through clear goal-setting, regular feedback, and development opportunities. \n\u2022 Foster a collaborative and inclusive team environment that encourages innovation and iterative learning. \n\u2022 Cross-Functional Collaboration:\n\u2022 Work closely with product management, software engineering, and data engineering teams to integrate machine learning models into larger software systems and product offerings. \n\u2022 Partner with stakeholders across the organization to understand business needs and translate them into technical requirements and actionable machine learning projects. \n\u2022 Project Management:\n\u2022 Oversee the full project lifecycle for multiple machine learning initiatives, from ideation and data collection to model development and deployment. \n\u2022 Manage resources, timelines, and risks effectively, ensuring that projects meet their objectives and are delivered on schedule. \n\u2022 Performance Monitoring and Model Optimization:\n\u2022 Continuously monitor the performance of deployed models, identifying opportunities for improvement and optimization. \n\u2022 Implement robust testing and validation strategies to ensure model accuracy and reliability over time.",
"word_count": 408
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Machine Learning"
},
{
"is_primary": true,
"skill_name": "Artificial Intelligence"
},
{
"is_primary": true,
"skill_name": "GPT"
},
{
"is_primary": true,
"skill_name": "LLaMA"
},
{
"is_primary": true,
"skill_name": "RAG"
},
{
"is_primary": true,
"skill_name": "LangChain"
},
{
"is_primary": true,
"skill_name": "Predictive Analytics"
},
{
"is_primary": true,
"skill_name": "Data Mining"
},
{
"is_primary": true,
"skill_name": "Model Deployment"
},
{
"is_primary": true,
"skill_name": "Testing"
},
{
"is_primary": true,
"skill_name": "Validation"
}
],
"jd_role": {
"display_name": "Machine Learning Engineer",
"rationale": null,
"role_aliases": [
"ML Engineer",
"Machine Learning Developer",
"Data Scientist"
],
"role_archetype": "Engineering",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": null,
"certifications": [],
"company_name": "Uber",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"Tech Consulting",
"Software Development"
],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE - Computer Science",
"raw": "Education: Bachelor\u0027s degree in Computer Science, Engineering, or a closely related field.",
"requirement": "required"
},
{
"level": "Master\u0027s",
"qualification": "MTECH/ME/MSC - Software Engineering / Machine Learning",
"raw": "Advanced degrees (Master\u0027s or PhD) in fields that emphasize software engineering or machine learning are preferred.",
"requirement": "preferred"
}
],
"experience": {
"max": null,
"min": 7,
"raw": "At least 7 years of experience in software development"
},
"job_locations": [],
"role": "Machine Learning Engineer",
"role_aliases": [
"ML Engineer",
"Machine Learning Developer",
"Data Scientist"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 12,
"heading": "What You\u0027ll Do",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Apply leading Models to solve",
"last_5_words": "accuracy and reliability over time."
},
"text": "\u2022 Apply leading Models to solve business problems\n\u2022 Use models from GPT-* to LLama-* to solve wide range of business problems from sales tech, employee productivity, coding and automation\n\u2022 Build RAG frameworks \n\u2022 Build Langchain modules\n\u2022 Model Development and Implementation:\n\u2022 Design, develop, and deploy machine learning models to solve complex problems and improve user experiences, operational efficiency, or system performance. \n\u2022 Utilize a variety of data sources, types, and structures to extract actionable insights through predictive analytics and data mining techniques. \n\u2022 Research and Innovation:\n\u2022 Stay abreast of the latest developments in the field of machine learning and artificial intelligence. Evaluate emerging trends and technologies for potential adoption to maintain and expand competitive advantage. \n\u2022 Lead research initiatives that test new algorithms, evaluate new methodologies, and explore innovative uses of data that can lead to scalable solutions. \n\u2022 Team Leadership and Development:\n\u2022 Lead and mentor a team of machine learning engineers and data scientists. Ensure the continuous professional growth of team members through clear goal-setting, regular feedback, and development opportunities. \n\u2022 Foster a collaborative and inclusive team environment that encourages innovation and iterative learning. \n\u2022 Cross-Functional Collaboration:\n\u2022 Work closely with product management, software engineering, and data engineering teams to integrate machine learning models into larger software systems and product offerings. \n\u2022 Partner with stakeholders across the organization to understand business needs and translate them into technical requirements and actionable machine learning projects. \n\u2022 Project Management:\n\u2022 Oversee the full project lifecycle for multiple machine learning initiatives, from ideation and data collection to model development and deployment. \n\u2022 Manage resources, timelines, and risks effectively, ensuring that projects meet their objectives and are delivered on schedule. \n\u2022 Performance Monitoring and Model Optimization:\n\u2022 Continuously monitor the performance of deployed models, identifying opportunities for improvement and optimization. \n\u2022 Implement robust testing and validation strategies to ensure model accuracy and reliability over time.",
"word_count": 408
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "f8694a18-bf95-438b-8fd6-f2c6f86ce65a",
"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
},
{
"display_name": "Data Scientist",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 49,
"score": 1.0,
"slug": "data-scientist",
"total_count": null
}
],
"kra_match_roles": [
{
"display_name": "ML Engineer",
"kra_matches": [
{
"kra_text": "Translates product requirements into machine learning system specifications including feature definitions, model architecture choices, and success metric definitions.",
"sentence": "Partner with stakeholders across the organization to understand business needs and translate them into technical requirements and actionable machine learning projects.",
"similarity": 0.6289
},
{
"kra_text": "Monitors production model behavior for data drift, concept drift, and prediction performance degradation using monitoring dashboards and alerting.",
"sentence": "Continuously monitor the performance of deployed models, identifying opportunities for improvement and optimization.",
"similarity": 0.6076
},
{
"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 deploy machine learning models to solve complex problems and improve user experiences, operational efficiency, or system performance.",
"similarity": 0.5923
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 3,
"score": 0.6096,
"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": "Oversee the full project lifecycle for multiple machine learning initiatives, from ideation and data collection to model development and deployment.",
"similarity": 0.5946
},
{
"kra_text": "Sets up model monitoring dashboards, data drift detection, prediction performance tracking, and alert routing for production ML systems.",
"sentence": "Continuously monitor the performance of deployed models, identifying opportunities for improvement and optimization.",
"similarity": 0.5824
},
{
"kra_text": "Sets up model monitoring dashboards, data drift detection, prediction performance tracking, and alert routing for production ML systems.",
"sentence": "Design, develop, and deploy machine learning models to solve complex problems and improve user experiences, operational efficiency, or system performance.",
"similarity": 0.5383
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 16,
"score": 0.5718,
"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": "Partner with stakeholders across the organization to understand business needs and translate them into technical requirements and actionable machine learning projects.",
"similarity": 0.5599
},
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Design, develop, and deploy machine learning models to solve complex problems and improve user experiences, operational efficiency, or system performance.",
"similarity": 0.548
},
{
"kra_text": "Implements data transformation, cleansing, deduplication, and enrichment logic to convert raw source data into analytics-ready curated datasets.",
"sentence": "Utilize a variety of data sources, types, and structures to extract actionable insights through predictive analytics and data mining techniques.",
"similarity": 0.5432
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.5504,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "Scala Backend Developer",
"kra_matches": [
{
"kra_text": "performance and reliability tuning",
"sentence": "Performance Monitoring and Model Optimization:",
"similarity": 0.6233
},
{
"kra_text": "application data modeling",
"sentence": "Apply leading Models to solve business problems",
"similarity": 0.5283
},
{
"kra_text": "performance and reliability tuning",
"sentence": "Continuously monitor the performance of deployed models, identifying opportunities for improvement and optimization.",
"similarity": 0.4898
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 87,
"score": 0.5471,
"slug": "scala-backend-developer",
"total_count": null
},
{
"display_name": "AI Engineer",
"kra_matches": [
{
"kra_text": "Optimizes AI pipeline efficiency by tuning model selection, context window usage, prompt caching, and batching strategies to reduce cost and latency.",
"sentence": "Performance Monitoring and Model Optimization:",
"similarity": 0.5438
},
{
"kra_text": "Translates product requirements into AI-powered features by integrating large language models like GPT-4, Claude, or Gemini into application workflows via API.",
"sentence": "Use models from GPT-* to LLama-* to solve wide range of business problems from sales tech, employee productivity, coding and automation",
"similarity": 0.5394
},
{
"kra_text": "Optimizes AI pipeline efficiency by tuning model selection, context window usage, prompt caching, and batching strategies to reduce cost and latency.",
"sentence": "Continuously monitor the performance of deployed models, identifying opportunities for improvement and optimization.",
"similarity": 0.5363
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 13,
"score": 0.5398,
"slug": "ai-engineer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "ML Engineer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"LangChain",
"Machine Learning"
],
"role_id": 3,
"score": 0.1818,
"slug": "ml-engineer",
"total_count": 11
},
{
"display_name": "AI Engineer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"LangChain",
"Machine Learning"
],
"role_id": 13,
"score": 0.1818,
"slug": "ai-engineer",
"total_count": 11
},
{
"display_name": "MLOps Engineer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"LangChain",
"Machine Learning"
],
"role_id": 16,
"score": 0.1818,
"slug": "ml-ops-engineer",
"total_count": 11
}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "DOMAIN",
"chosen_role": {
"display_name": "LLM / GenAI Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 151,
"score": 0.95,
"slug": "llm-genai-engineer",
"total_count": null
},
"confidence": 0.95,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [
"Generative AI application development",
"LLM-based business problem solving",
"RAG system design",
"AI model research and innovation",
"Cross-functional ML integration",
"Model performance monitoring",
"Team leadership and mentoring",
"Project lifecycle management"
],
"matched_kras": [
"Apply leading Models to solve business problems",
"Build RAG frameworks",
"Build Langchain modules",
"Design, develop, and deploy machine learning models",
"Lead research initiatives that test new algorithms",
"Lead and mentor a team of machine learning engineers",
"Integrate machine learning models into larger software systems",
"Oversee the full project lifecycle for multiple machine learning initiatives",
"Continuously monitor the performance of deployed models",
"Implement robust testing and validation strategies"
],
"matched_skills": [
"GPT-*",
"LLama-*",
"RAG frameworks",
"Langchain",
"machine learning models",
"predictive analytics",
"data mining",
"model development",
"model deployment",
"testing and validation"
],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Domain=AI / ML; The JD is centered on GPT/LLama models, RAG, and LangChain for business applications, which most closely matches LLM/GenAI engineering.",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 4,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": {
"best_kra_similarity": 0.0,
"queue_id": 775,
"r_and_r_preview": "\u2022 Apply leading Models to solve business problems\n\u2022 Use models from GPT-* to LLama-* to solve wide range of business problems from sales tech, employee productivity, coding and automation\n\u2022 Build RAG ",
"role_display_name": "LLM / GenAI Engineer",
"role_slug": "llm-genai-engineer",
"status": "pending"
},
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 11586,
"role_display_name": "LLM / GenAI Engineer",
"role_slug": "llm-genai-engineer",
"skill_name": "GPT",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 11587,
"role_display_name": "LLM / GenAI Engineer",
"role_slug": "llm-genai-engineer",
"skill_name": "LLaMA",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 11588,
"role_display_name": "LLM / GenAI Engineer",
"role_slug": "llm-genai-engineer",
"skill_name": "Predictive Analytics",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 11589,
"role_display_name": "LLM / GenAI Engineer",
"role_slug": "llm-genai-engineer",
"skill_name": "Data Mining",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 11590,
"role_display_name": "LLM / GenAI Engineer",
"role_slug": "llm-genai-engineer",
"skill_name": "Model Deployment",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 11591,
"role_display_name": "LLM / GenAI Engineer",
"role_slug": "llm-genai-engineer",
"skill_name": "Testing",
"status": "pending"
},
{
"is_primary": true,
"queue_id": 11592,
"role_display_name": "LLM / GenAI Engineer",
"role_slug": "llm-genai-engineer",
"skill_name": "Validation",
"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": 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": 2016,
"existing_alias_text": "Artificial Intelligence",
"input_term": "Artificial Intelligence",
"matched_canonical": {
"category_id": 2,
"display_name": "Artificial Intelligence",
"id": 1357,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "artificial-intelligence",
"sub_category_id": 1020,
"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": 1830,
"existing_alias_text": "RAG",
"input_term": "RAG",
"matched_canonical": {
"category_id": 2,
"display_name": "RAG",
"id": 1194,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "rag",
"sub_category_id": 904,
"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": 501,
"existing_alias_text": "LangChain",
"input_term": "LangChain",
"matched_canonical": {
"category_id": 5,
"display_name": "LangChain",
"id": 240,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "langchain",
"sub_category_id": 146,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"matched_via": "alias"
}
],
"candidate_roles": [
{
"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"
}
],
"chosen_role": {
"display_name": "LLM / GenAI Engineer",
"id": 151,
"rationale": "Domain=AI / ML; The JD is centered on GPT/LLama models, RAG, and LangChain for business applications, which most closely matches LLM/GenAI engineering.",
"role_archetype": null,
"slug": "llm-genai-engineer",
"source": "db"
},
"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": []
},
{
"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": "Artificial Intelligence",
"llm_role": null,
"roles_from_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": "RAG",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "LLM Operations and Orchestration",
"id": 49,
"rationale": "Operational stack for building, serving, evaluating, and orchestrating LLM-based systems. This includes vector retrieval, prompt workflows, LLM serving, and observability for generative applications.",
"slug": "llm-operations-and-orchestration",
"source": "db"
},
"input_skill": "LangChain",
"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"
}
]
}
],
"input_final_skills": [
"Machine Learning",
"Artificial Intelligence",
"GPT",
"LLaMA",
"RAG",
"LangChain",
"Predictive Analytics",
"Data Mining",
"Model Deployment",
"Testing",
"Validation"
],
"input_llm_skills": [
"Machine Learning",
"Artificial Intelligence",
"GPT",
"LLaMA",
"RAG",
"LangChain",
"Predictive Analytics",
"Data Mining",
"Model Deployment",
"Testing",
"Validation"
],
"new_aliases_persisted": 0,
"run_id": "f8694a18-bf95-438b-8fd6-f2c6f86ce65a",
"skills_detail": [
{
"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": [
{
"alias_text": "Artificial Intelligence",
"alias_type": "CANONICAL",
"id": 2016,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 2,
"display_name": "Artificial Intelligence",
"id": 1357,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "artificial-intelligence",
"sub_category_id": 1020,
"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": "Artificial Intelligence",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Artificial Intelligence",
"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": "GPT",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Machine Learning Frameworks",
"skill_nature": "TOOL",
"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": "gpt",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "LLaMA",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Machine Learning Frameworks",
"skill_nature": "TOOL",
"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": "llama",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "RAG",
"alias_type": "CANONICAL",
"id": 1830,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 2,
"display_name": "RAG",
"id": 1194,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "rag",
"sub_category_id": 904,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"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": "RAG",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "RAG",
"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": "LangChain",
"alias_type": "CANONICAL",
"id": 501,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 5,
"display_name": "LangChain",
"id": 240,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "langchain",
"sub_category_id": 146,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "LLM Operations and Orchestration",
"id": 49,
"rationale": "Operational stack for building, serving, evaluating, and orchestrating LLM-based systems. This includes vector retrieval, prompt workflows, LLM serving, and observability for generative applications.",
"slug": "llm-operations-and-orchestration",
"source": "db"
},
"input_skill": "LangChain",
"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"
}
]
}
],
"input_skill": "LangChain",
"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": "Predictive Analytics",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering 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": "predictive-analytics",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Data Mining",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering 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": "data-mining",
"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": "Testing",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Testing 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": "testing",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "Validation",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Testing 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": "validation",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"GPT",
"LLaMA",
"Predictive Analytics",
"Data Mining",
"Model Deployment",
"Testing",
"Validation"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "LLM / GenAI Engineer",
"id": 151,
"rationale": "Domain=AI / ML; The JD is centered on GPT/LLama models, RAG, and LangChain for business applications, which most closely matches LLM/GenAI engineering.",
"role_archetype": null,
"slug": "llm-genai-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Machine Learning",
"tag": "in_db"
},
{
"skill": "Artificial Intelligence",
"tag": "in_db"
},
{
"skill": "GPT",
"tag": "new"
},
{
"skill": "LLaMA",
"tag": "new"
},
{
"skill": "RAG",
"tag": "in_db"
},
{
"skill": "LangChain",
"tag": "in_db"
},
{
"skill": "Predictive Analytics",
"tag": "new"
},
{
"skill": "Data Mining",
"tag": "new"
},
{
"skill": "Model Deployment",
"tag": "new"
},
{
"skill": "Testing",
"tag": "new"
},
{
"skill": "Validation",
"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": 151,
"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"
},
"dimension_id": 50,
"input_skill": "Machine Learning",
"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"
},
{
"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": 1356,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 151,
"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"
},
"dimension_id": 96,
"input_skill": "Machine Learning",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 1356,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 151,
"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"
},
"dimension_id": 96,
"input_skill": "Artificial Intelligence",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 1357,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 151,
"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"
},
"dimension_id": 96,
"input_skill": "RAG",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 1194,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 151,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "LLM Operations and Orchestration",
"id": 49,
"rationale": "Operational stack for building, serving, evaluating, and orchestrating LLM-based systems. This includes vector retrieval, prompt workflows, LLM serving, and observability for generative applications.",
"slug": "llm-operations-and-orchestration",
"source": "db"
},
"dimension_id": 49,
"input_skill": "LangChain",
"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"
},
{
"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": 240,
"skill_tag": "in_db",
"skipped_reason": null
}
],
"new_skills_created": 0,
"role_dimension_saved": 0,
"skill_dimension_saved": 0,
"skipped": 0
},
"planner_output": null,
"run_id": "f8694a18-bf95-438b-8fd6-f2c6f86ce65a"
}
LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.