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Pipeline run

f8694a18-bf95-438b-8fd6-f2c6f86ce65a

Pipeline LLM cost (USD)
API 1: $0.0084 API 2: $0.0003 API 3: $0.0000 Total: $0.0087

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work · Machine Learning / AI
Build and deploy ML/LLM solutions using GPT/LLaMA, RAG, and LangChain to automate business workflows and improve product/user experience, then monitor and tune models for accuracy and reliability.
"Build RAG frameworks"
Tech stack maturity
Modern Cloud Native
The role centers on GenAI, LangChain, and RAG, which are typically built and deployed on modern cloud-native AI stacks.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
3.20 / 5
Title match
Has AI skill
AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2): LangChain, Ollama
Models / concepts (×3): RAG, LLM, AI, GenAI, Machine Learning, Artificial Intelligence
Evidence — skills matched in JD (11)
Machine Learning Artificial Intelligence GPT LLaMA RAG LangChain Predictive Analytics Data Mining Model Deployment Testing Validation
Skill cluster (2 dimension groups, role-scoped)
AI Governance and Model Security
Machine Learning
Cross-cutting / unaligned
Artificial Intelligence GPT LLaMA RAG LangChain Predictive Analytics Data Mining Model Deployment Testing Validation
Show KRA description ↓
• 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.

Signals

Skill ml-engineer
0.18
Alias ml-engineer
1.00
KRA ml-engineer
0.61

Post-classification

Centroidupdated · n=4
Alias collision log
New-role queue
New skills captured7
New KRA capturedyes

Captured for admin review

GPT primary LLM / GenAI Engineer pending
LLaMA primary LLM / GenAI Engineer pending
Predictive Analytics primary LLM / GenAI Engineer pending
Data Mining primary LLM / GenAI Engineer pending
Model Deployment primary LLM / GenAI Engineer pending
Testing primary LLM / GenAI Engineer pending
Validation primary LLM / GenAI Engineer pending
R&R fragment (sim 0.00) LLM / GenAI Engineer pending

• 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 …

Status: completed Created: 2026-05-27T14:53:40.353593Z Updated: 2026-06-12T17:08:56.801438Z API 3 duration: 7109 ms
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

LLM / GenAI Engineer

domain · AI / ML CASE DOMAIN

slug: 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

GPT-*LLama-*RAG frameworksLangchainmachine learning modelspredictive analyticsdata miningmodel developmentmodel deploymenttesting and validation

Matched dimensions

Generative AI application developmentLLM-based business problem solvingRAG system designAI model research and innovationCross-functional ML integrationModel performance monitoringTeam leadership and mentoringProject lifecycle management

Matched KRAs

Apply leading Models to solve business problemsBuild RAG frameworksBuild Langchain modulesDesign, develop, and deploy machine learning modelsLead research initiatives that test new algorithmsLead and mentor a team of machine learning engineersIntegrate machine learning models into larger software systemsOversee the full project lifecycle for multiple machine learning initiativesContinuously monitor the performance of deployed modelsImplement robust testing and validation strategies

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

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

Job description

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.

Machine Learning Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Machine Learning id=1356 · machine-learning

Aliases — catalog

  • Machine Learning (CANONICAL)

Context tags (catalog)

Keras PyTorch TensorFlow cross-validation data preprocessing ensemble methods feature engineering hyperparameter tuning model evaluation natural language processing neural networks reinforcement learning scikit-learn supervised learning unsupervised learning

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)
Artificial Intelligence Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Artificial Intelligence id=1357 · artificial-intelligence

Aliases — catalog

  • Artificial Intelligence (CANONICAL)

Context tags (catalog)

AI ethics PyTorch TensorFlow algorithm optimization computer vision data mining deep learning machine learning model training natural language processing neural networks predictive analytics reinforcement learning supervised learning unsupervised learning

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)
GPT Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
LLaMA Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
RAG Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: RAG id=1194 · rag

Aliases — catalog

  • RAG (CANONICAL)

Context tags (catalog)

AI applications contextualization data augmentation fine-tuning generation information retrieval knowledge integration machine learning model training natural language processing prompt engineering retrieval semantic search transformer models user intent

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)
LangChain Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: LangChain id=240 · langchain

Aliases — catalog

  • LangChain (CANONICAL) primary

Context tags (catalog)

API integration Hugging Face LLM LLMs OpenAI RAG agents callbacks chains data augmentation deployment document loaders embeddings fine-tuning memory prompt engineering prompt templates prompts retrieval retrievers state management streaming text splitters toolkits tools vector database vector stores

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)
Predictive Analytics Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Mining Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Data Engineering Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Model Deployment Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Testing Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Testing Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Validation Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
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
RoleMachine Learning Engineer
CompanyUber
ExperienceAt least 7 years of experience in software development
DomainIT Services & Consulting
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [],
  "company_name": "Uber",
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "Tech Consulting",
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      ],
      "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"
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  "jd_role": {
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    "rationale": null,
    "role_aliases": [
      "ML Engineer",
      "Machine Learning Developer",
      "Data Scientist"
    ],
    "role_archetype": "Engineering",
    "slug": ""
  },
  "nano_parsed": {
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    "about_company": null,
    "certifications": [],
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    "ctc": null,
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    "roles_and_responsibilities": [
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        "word_count": 408
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    "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,
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}
API 2 — extract-details
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          "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.

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