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Pipeline run

3bb0bfe3-c4a9-4f10-94b2-b5661d916a21

Pipeline LLM cost (USD)
API 1: $0.0078 API 2: $0.0005 API 3: $0.0000 Total: $0.0083

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
SPARSE JD sources · ai_index: jd · nature_of_work: jd · tech_stack_maturity: jd
Nature of work · Python ML/NLP + Computer Vision
Build Python-based ML/NLP and computer-vision workflows, analyze data with NumPy/Pandas/Matplotlib/Seaborn, and support Python web-framework work across Windows/Linux, using regex and NLP libraries like NLTK/spaCy.
"Experience in OpenCV"
Tech stack maturity
Mainstream Modern
ML engineering with Python and machine learning is a widely adopted, current stack typically centered on mainstream modern tooling rather than legacy or bleeding-edge infrastructure.
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):
Models / concepts (×3): NLP, Machine Learning
Evidence — skills matched in JD (16)
Python OpenCV Machine Learning NLP CNN LSTM NumPy Pandas Matplotlib Seaborn Windows Linux Web Framework NLTK spaCy Regular Expressions
Skill cluster (3 dimension groups, role-scoped)
AI Governance and Model Security
Machine Learning
Programming Languages for ML Systems
Python
Cross-cutting / unaligned
OpenCV NLP CNN LSTM NumPy Pandas Matplotlib Seaborn Windows Linux Web Framework NLTK spaCy Regular Expressions
Show KRA description ↓
1. Knowledge and hands-on in Python, 2. Experience in OpenCV, 3. Understanding of Machine Learning concepts - NLP, CNNs, LSTMs, 4. Experience with Data Analysis tools in Python such as Numpy, Pandas, Matplotlib, Seaborn. 5. Well versed in Windows & Linux, 6. Experience in at least one Python web development framework, 7. Understanding of Python NLP libraries such as NLTK & SpaCy, 8. Fluency in regular expressions.

Signals

Skill ml-ops-engineer
0.12
Alias backend-engineer
1.00
KRA ai-engineer
0.36

Post-classification

Centroidupdated · n=25
Alias collision log
New-role queue
New skills captured14
New KRA capturedyes

Captured for admin review

OpenCV primary ML Engineer pending
NLP primary ML Engineer pending
CNN primary ML Engineer pending
LSTM primary ML Engineer pending
NumPy primary ML Engineer pending
Pandas primary ML Engineer pending
Matplotlib primary ML Engineer pending
Seaborn primary ML Engineer pending
Windows primary ML Engineer pending
Linux primary ML Engineer pending
Web Framework primary ML Engineer pending
NLTK primary ML Engineer pending
spaCy primary ML Engineer pending
Regular Expressions primary ML Engineer pending
R&R fragment (sim 0.35) ML Engineer pending

1. Knowledge and hands-on in Python, 2. Experience in OpenCV, 3. Understanding of Machine Learning concepts - NLP, CNNs, LSTMs, 4. Experience with Data Analysis tools in Python such as Numpy, Pandas, …

Status: completed Created: 2026-05-27T15:57:26.215942Z Updated: 2026-06-12T15:41:05.928011Z API 3 duration: 25890 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

ML Engineer

domain · AI / ML CASE DOMAIN

slug: ml-engineer · id: 3 · source: db

Domain=AI / ML; The JD centers on Python-based machine learning work with NLP, CNNs/LSTMs, and NLP libraries, which best matches an ML Engineer rather than a pure data scientist or computer vision specialist.

Matched skills

PythonOpenCVMachine LearningNLPCNNsLSTMsNumpyPandasMatplotlibSeabornWindowsLinuxNLTKSpaCyregular expressions

Matched dimensions

Python DevelopmentMachine Learning ConceptsComputer VisionData Analysis in PythonNLP EngineeringWeb Framework DevelopmentCross-platform Development

Matched KRAs

Knowledge and hands-on in PythonExperience in OpenCVUnderstanding of Machine Learning concepts - NLP, CNNs, LSTMsExperience with Data Analysis tools in PythonWell versed in Windows & LinuxExperience in at least one Python web development frameworkUnderstanding of Python NLP libraries such as NLTK & SpaCyFluency in regular expressions

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

We are hiring for Python Developer.


Experience:- 2 + Years


Location:- Pune


Job Description:


Requirements:
1. Knowledge and hands-on in Python,
2. Experience in OpenCV,
3. Understanding of Machine Learning concepts - NLP, CNNs, LSTMs,
4. Experience with Data Analysis tools in Python such as Numpy, Pandas, Matplotlib, Seaborn.
5. Well versed in Windows & Linux,
6. Experience in at least one Python web development framework,
7. Understanding of Python NLP libraries such as NLTK & SpaCy,
8. Fluency in regular expressions.


Interested candidates share resume on sayaji@expediteinformatics.com or call us on 9665566357


Contact:- Sayaji Patil

Skills from this JD

Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.

Python Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Python id=5 · python

Aliases — catalog

  • Python (CANONICAL) primary
  • Python 2 (VERSION)
  • Python 2.x (VERSION)
  • Python 3 (VERSION)
  • Python 3.10 (VERSION)
  • Python 3.11 (VERSION)
  • Python 3.12 (VERSION)
  • Python 3.x (VERSION)
  • py (VERSION)
  • py2 (VERSION)
  • py3 (VERSION)
  • python 3 (VERSION)
  • python 3.x (VERSION)
  • python2 (VERSION)
  • python3 (VERSION)
  • python3.x (VERSION)

Context tags (catalog)

API Django FastAPI Flask Jupyter NumPy PEP 8 Pandas REST SQLAlchemy asyncio pandas pip pytest type hints venv virtualenv

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
PSF
License
mit
Year introduced
1991
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
3

Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Security Scripting & DSL Languages Catalog dimension db id 248

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer

  • Programming Languages & DSLs Catalog dimension db id 475

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

  • Programming Languages and Scripting Catalog dimension db id 59

    Library dimension (catalog)

    Roles linked in library: Cyber Security Engineer

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

  • Programming Languages for XR Catalog dimension db id 97

    Library dimension (catalog)

    Roles linked in library: AR/VR Engineer

  • Python Programming Catalog dimension db id 290

    Library dimension (catalog)

    Roles linked in library: Python Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension saved
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
OpenCV 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
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 saved
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
NLP 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
Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
CNN 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
Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
LSTM 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
Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
NumPy 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
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Pandas 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
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Matplotlib 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
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Seaborn 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
TOOL
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Windows 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
Operating Systems
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Linux 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
Operating Systems
Sub-category
general
Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Web Framework 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
Web Frameworks
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
NLTK 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
spaCy 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
Regular Expressions 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
Concepts
Sub-category
general
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
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
Python in_db
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension saved
Python in_db
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Machine Learning in_db
AI Governance and Model Security
ai-governance-and-model-security
Existing dimension (library) · Role↔dimension saved
Machine Learning in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed OpenCV | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed NLP | type=Concepts subtype=general nature=CONCEPT lifespan=EVERGREEN
canonical_skill_proposed CNN | type=Concepts subtype=general nature=CONCEPT lifespan=EVERGREEN
canonical_skill_proposed LSTM | type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed NumPy | type=Data Engineering Tools subtype=general nature=TOOL lifespan=EVERGREEN
canonical_skill_proposed Pandas | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Matplotlib | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Seaborn | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Windows | type=Operating Systems subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Linux | type=Operating Systems subtype=general nature=PLATFORM lifespan=EVERGREEN
canonical_skill_proposed Web Framework | type=Web Frameworks subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed NLTK | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed spaCy | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Regular Expressions | type=Concepts subtype=general nature=CONCEPT lifespan=EVERGREEN
nano JD Parser — gpt-4.1-nano click to toggle
RolePython Developer
Experience2 + Years
DomainOther
Location Pune, India
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [],
  "company_name": null,
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [],
      "domain": "Other"
    },
    "secondary": null
  },
  "education": [],
  "experience": {
    "max": null,
    "min": 2,
    "raw": "2 + Years"
  },
  "job_locations": [
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      "aliases": [],
      "city": "Pune",
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      "state": null,
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    }
  ],
  "role": "Python Developer",
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  "role_archetype": "Engineering",
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      "bullet_count": 8,
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      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "1. Knowledge and hands-on in",
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      },
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      "word_count": 66
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Python"
    },
    {
      "is_primary": true,
      "skill_name": "OpenCV"
    },
    {
      "is_primary": true,
      "skill_name": "Machine Learning"
    },
    {
      "is_primary": true,
      "skill_name": "NLP"
    },
    {
      "is_primary": true,
      "skill_name": "CNN"
    },
    {
      "is_primary": true,
      "skill_name": "LSTM"
    },
    {
      "is_primary": true,
      "skill_name": "NumPy"
    },
    {
      "is_primary": true,
      "skill_name": "Pandas"
    },
    {
      "is_primary": true,
      "skill_name": "Matplotlib"
    },
    {
      "is_primary": true,
      "skill_name": "Seaborn"
    },
    {
      "is_primary": true,
      "skill_name": "Windows"
    },
    {
      "is_primary": true,
      "skill_name": "Linux"
    },
    {
      "is_primary": true,
      "skill_name": "Web Framework"
    },
    {
      "is_primary": true,
      "skill_name": "NLTK"
    },
    {
      "is_primary": true,
      "skill_name": "spaCy"
    },
    {
      "is_primary": true,
      "skill_name": "Regular Expressions"
    }
  ],
  "jd_role": {
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    "rationale": null,
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    "slug": ""
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  "nano_parsed": {
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    "experience": {
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        "source_marker": {
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        },
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  "rejected": false,
  "rejection_reason": null,
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  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
      {
        "display_name": "Backend Developer",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 1,
        "score": 1.0,
        "slug": "backend-engineer",
        "total_count": null
      },
      {
        "display_name": "Python Backend Developer",
        "kra_matches": null,
        "matched_count": null,
        "matched_skills": null,
        "role_id": 80,
        "score": 1.0,
        "slug": "python-backend-developer",
        "total_count": null
      }
    ],
    "kra_match_roles": [
      {
        "display_name": "AI Engineer",
        "kra_matches": [
          {
            "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": "Understanding of Machine Learning concepts - NLP, CNNs, LSTMs,",
            "similarity": 0.3776
          },
          {
            "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": "Understanding of Python NLP libraries such as NLTK \u0026 SpaCy,",
            "similarity": 0.3345
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 13,
        "score": 0.356,
        "slug": "ai-engineer",
        "total_count": null
      },
      {
        "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": "Understanding of Machine Learning concepts - NLP, CNNs, LSTMs,",
            "similarity": 0.4182
          },
          {
            "kra_text": "Designs end-to-end ML training pipelines and model inference workflows using TensorFlow, PyTorch, or scikit-learn on cloud ML platforms.",
            "sentence": "Understanding of Python NLP libraries such as NLTK \u0026 SpaCy,",
            "similarity": 0.2744
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 3,
        "score": 0.3463,
        "slug": "ml-engineer",
        "total_count": null
      },
      {
        "display_name": "AI Compliance Officer",
        "kra_matches": [
          {
            "kra_text": "Evaluates AI models for bias in protected attributes, explainability limitations, and transparency requirements in automated decision-making contexts.",
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            "similarity": 0.3693
          },
          {
            "kra_text": "Evaluates AI models for bias in protected attributes, explainability limitations, and transparency requirements in automated decision-making contexts.",
            "sentence": "Understanding of Python NLP libraries such as NLTK \u0026 SpaCy,",
            "similarity": 0.2377
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 12,
        "score": 0.3035,
        "slug": "ai-compliance-officer",
        "total_count": null
      },
      {
        "display_name": "MLOps Engineer",
        "kra_matches": [
          {
            "kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
            "sentence": "Understanding of Machine Learning concepts - NLP, CNNs, LSTMs,",
            "similarity": 0.3571
          },
          {
            "kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
            "sentence": "Understanding of Python NLP libraries such as NLTK \u0026 SpaCy,",
            "similarity": 0.2069
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 16,
        "score": 0.282,
        "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": "Understanding of Machine Learning concepts - NLP, CNNs, LSTMs,",
            "similarity": 0.2754
          },
          {
            "kra_text": "Develops batch and real-time streaming data pipelines using Apache Spark, Apache Kafka, Apache Flink, or Airflow for data movement and processing at scale.",
            "sentence": "Understanding of Python NLP libraries such as NLTK \u0026 SpaCy,",
            "similarity": 0.2278
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 2,
        "score": 0.2516,
        "slug": "data-engineer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "MLOps Engineer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "Machine Learning",
          "Python"
        ],
        "role_id": 16,
        "score": 0.125,
        "slug": "ml-ops-engineer",
        "total_count": 16
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "Machine Learning",
          "Python"
        ],
        "role_id": 3,
        "score": 0.125,
        "slug": "ml-engineer",
        "total_count": 16
      },
      {
        "display_name": "Cyber Security Engineer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "Python"
        ],
        "role_id": 5,
        "score": 0.0625,
        "slug": "cybersecurity-engineer",
        "total_count": 16
      },
      {
        "display_name": "AR/VR Engineer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "Python"
        ],
        "role_id": 8,
        "score": 0.0625,
        "slug": "ar-vr-engineer",
        "total_count": 16
      },
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 1,
        "matched_skills": [
          "Python"
        ],
        "role_id": 2,
        "score": 0.0625,
        "slug": "data-engineer",
        "total_count": 16
      }
    ]
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "DOMAIN",
    "chosen_role": {
      "display_name": "ML Engineer",
      "kra_matches": null,
      "matched_count": null,
      "matched_skills": null,
      "role_id": 3,
      "score": 0.93,
      "slug": "ml-engineer",
      "total_count": null
    },
    "confidence": 0.93,
    "is_new_role": false,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "matched_dimensions": [
      "Python Development",
      "Machine Learning Concepts",
      "Computer Vision",
      "Data Analysis in Python",
      "NLP Engineering",
      "Web Framework Development",
      "Cross-platform Development"
    ],
    "matched_kras": [
      "Knowledge and hands-on in Python",
      "Experience in OpenCV",
      "Understanding of Machine Learning concepts - NLP, CNNs, LSTMs",
      "Experience with Data Analysis tools in Python",
      "Well versed in Windows \u0026 Linux",
      "Experience in at least one Python web development framework",
      "Understanding of Python NLP libraries such as NLTK \u0026 SpaCy",
      "Fluency in regular expressions"
    ],
    "matched_skills": [
      "Python",
      "OpenCV",
      "Machine Learning",
      "NLP",
      "CNNs",
      "LSTMs",
      "Numpy",
      "Pandas",
      "Matplotlib",
      "Seaborn",
      "Windows",
      "Linux",
      "NLTK",
      "SpaCy",
      "regular expressions"
    ],
    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Domain=AI / ML; The JD centers on Python-based machine learning work with NLP, CNNs/LSTMs, and NLP libraries, which best matches an ML Engineer rather than a pure data scientist or computer vision specialist.",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 25,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": {
      "best_kra_similarity": 0.3463,
      "queue_id": 1265,
      "r_and_r_preview": "1. Knowledge and hands-on in Python,\n2. Experience in OpenCV,\n3. Understanding of Machine Learning concepts - NLP, CNNs, LSTMs,\n4. Experience with Data Analysis tools in Python such as Numpy, Pandas, ",
      "role_display_name": "ML Engineer",
      "role_slug": "ml-engineer",
      "status": "pending"
    },
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 17464,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "OpenCV",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 17465,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "NLP",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 17466,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "CNN",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 17467,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "LSTM",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 17468,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "NumPy",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 17469,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "Pandas",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 17470,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "Matplotlib",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 17471,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "Seaborn",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 17472,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "Windows",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 17473,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "Linux",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 17474,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "Web Framework",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 17475,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "NLTK",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 17476,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "spaCy",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 17477,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "Regular Expressions",
        "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": 67,
      "existing_alias_text": "Python",
      "input_term": "Python",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "Python",
        "id": 5,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "python",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 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"
    }
  ],
  "candidate_roles": [
    {
      "display_name": "Cloud Security Engineer",
      "id": 23,
      "rationale": null,
      "role_archetype": null,
      "slug": "cloud-security-engineer",
      "source": "db"
    },
    {
      "display_name": "Backend Developer",
      "id": 1,
      "rationale": null,
      "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
      "slug": "backend-engineer",
      "source": "db"
    },
    {
      "display_name": "Fullstack Developer",
      "id": 15,
      "rationale": null,
      "role_archetype": null,
      "slug": "full-stack-engineer",
      "source": "db"
    },
    {
      "display_name": "Fullstack Developer",
      "id": 435,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "fullstack-developer",
      "source": "db"
    },
    {
      "display_name": "Engineering Manager",
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      "rationale": null,
      "role_archetype": null,
      "slug": "engineering-manager",
      "source": "db"
    },
    {
      "display_name": "Cyber Security Engineer",
      "id": 5,
      "rationale": null,
      "role_archetype": null,
      "slug": "cybersecurity-engineer",
      "source": "db"
    },
    {
      "display_name": "Data Engineer",
      "id": 2,
      "rationale": null,
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      "slug": "data-engineer",
      "source": "db"
    },
    {
      "display_name": "ML Engineer",
      "id": 3,
      "rationale": null,
      "role_archetype": null,
      "slug": "ml-engineer",
      "source": "db"
    },
    {
      "display_name": "MLOps Engineer",
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      "rationale": null,
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      "slug": "ml-ops-engineer",
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    },
    {
      "display_name": "AR/VR Engineer",
      "id": 8,
      "rationale": null,
      "role_archetype": null,
      "slug": "ar-vr-engineer",
      "source": "db"
    },
    {
      "display_name": "Python Backend Developer",
      "id": 80,
      "rationale": null,
      "role_archetype": "Engineering",
      "slug": "python-backend-developer",
      "source": "db"
    },
    {
      "display_name": "AI Engineer",
      "id": 13,
      "rationale": null,
      "role_archetype": null,
      "slug": "ai-engineer",
      "source": "db"
    }
  ],
  "chosen_role": {
    "display_name": "ML Engineer",
    "id": 3,
    "rationale": "Domain=AI / ML; The JD centers on Python-based machine learning work with NLP, CNNs/LSTMs, and NLP libraries, which best matches an ML Engineer rather than a pure data scientist or computer vision specialist.",
    "role_archetype": null,
    "slug": "ml-engineer",
    "source": "db"
  },
  "dimensions": [
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Cloud Security Scripting \u0026 DSL Languages",
        "id": 248,
        "rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
        "slug": "cloud-security-scripting-dsl-languages",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cloud Security Engineer",
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          "slug": "cloud-security-engineer",
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        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages",
        "id": 1,
        "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
        "slug": "programming-languages",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Backend Developer",
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          "rationale": null,
          "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
          "slug": "backend-engineer",
          "source": "db"
        },
        {
          "display_name": "Fullstack Developer",
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          "slug": "full-stack-engineer",
          "source": "db"
        },
        {
          "display_name": "Fullstack Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
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          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages \u0026 DSLs",
        "id": 475,
        "rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
        "slug": "programming-languages-dsls",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Engineering Manager",
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          "slug": "engineering-manager",
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        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages and Scripting",
        "id": 59,
        "rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
        "slug": "programming-languages-and-scripting",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Cyber Security Engineer",
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          "rationale": null,
          "role_archetype": null,
          "slug": "cybersecurity-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for Data Work",
        "id": 21,
        "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
        "slug": "programming-languages-for-data-work",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Data Engineer",
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          "rationale": null,
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          "slug": "data-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for ML Systems",
        "id": 39,
        "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
        "slug": "programming-languages-for-ml-systems",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "ML Engineer",
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          "rationale": null,
          "role_archetype": null,
          "slug": "ml-engineer",
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        },
        {
          "display_name": "MLOps Engineer",
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          "slug": "ml-ops-engineer",
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        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Programming Languages for XR",
        "id": 97,
        "rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
        "slug": "programming-languages-for-xr",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "AR/VR Engineer",
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          "rationale": null,
          "role_archetype": null,
          "slug": "ar-vr-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Python Programming",
        "id": 290,
        "rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
        "slug": "python-programming",
        "source": "db"
      },
      "input_skill": "Python",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Python Backend Developer",
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          "rationale": null,
          "role_archetype": "Engineering",
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        }
      ]
    },
    {
      "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",
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        },
        {
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        },
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          "display_name": "MLOps Engineer",
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          "slug": "ml-ops-engineer",
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      ]
    },
    {
      "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_final_skills": [
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    "OpenCV",
    "Machine Learning",
    "NLP",
    "CNN",
    "LSTM",
    "NumPy",
    "Pandas",
    "Matplotlib",
    "Seaborn",
    "Windows",
    "Linux",
    "Web Framework",
    "NLTK",
    "spaCy",
    "Regular Expressions"
  ],
  "input_llm_skills": [
    "Python",
    "OpenCV",
    "Machine Learning",
    "NLP",
    "CNN",
    "LSTM",
    "NumPy",
    "Pandas",
    "Matplotlib",
    "Seaborn",
    "Windows",
    "Linux",
    "Web Framework",
    "NLTK",
    "spaCy",
    "Regular Expressions"
  ],
  "new_aliases_persisted": 0,
  "run_id": "3bb0bfe3-c4a9-4f10-94b2-b5661d916a21",
  "skills_detail": [
    {
      "aliases_in_db": [
        {
          "alias_text": "Python",
          "alias_type": "CANONICAL",
          "id": 67,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 2",
          "alias_type": "VERSION",
          "id": 72,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Python 2.x",
          "alias_type": "VERSION",
          "id": 74,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
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      "input_skill": "Regular Expressions",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Concepts",
          "skill_nature": "CONCEPT",
          "sub_category": "general",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "UNVERSIONED",
          "volatility": "STABLE"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "regular-expressions",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "OpenCV",
    "NLP",
    "CNN",
    "LSTM",
    "NumPy",
    "Pandas",
    "Matplotlib",
    "Seaborn",
    "Windows",
    "Linux",
    "Web Framework",
    "NLTK",
    "spaCy",
    "Regular Expressions"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "ML Engineer",
    "id": 3,
    "rationale": "Domain=AI / ML; The JD centers on Python-based machine learning work with NLP, CNNs/LSTMs, and NLP libraries, which best matches an ML Engineer rather than a pure data scientist or computer vision specialist.",
    "role_archetype": null,
    "slug": "ml-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "OpenCV",
      "tag": "new"
    },
    {
      "skill": "Machine Learning",
      "tag": "in_db"
    },
    {
      "skill": "NLP",
      "tag": "new"
    },
    {
      "skill": "CNN",
      "tag": "new"
    },
    {
      "skill": "LSTM",
      "tag": "new"
    },
    {
      "skill": "NumPy",
      "tag": "new"
    },
    {
      "skill": "Pandas",
      "tag": "new"
    },
    {
      "skill": "Matplotlib",
      "tag": "new"
    },
    {
      "skill": "Seaborn",
      "tag": "new"
    },
    {
      "skill": "Windows",
      "tag": "new"
    },
    {
      "skill": "Linux",
      "tag": "new"
    },
    {
      "skill": "Web Framework",
      "tag": "new"
    },
    {
      "skill": "NLTK",
      "tag": "new"
    },
    {
      "skill": "spaCy",
      "tag": "new"
    },
    {
      "skill": "Regular Expressions",
      "tag": "new"
    }
  ],
  "llm_cost_api1_usd": null,
  "llm_cost_api2_usd": null,
  "llm_cost_api3_usd": null,
  "llm_cost_total_usd": null,
  "persistence": {
    "items": [
      {
        "chosen_role_id": 3,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Security Scripting \u0026 DSL Languages",
          "id": 248,
          "rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
          "slug": "cloud-security-scripting-dsl-languages",
          "source": "db"
        },
        "dimension_id": 248,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Security Engineer",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 3,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages",
          "id": 1,
          "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
          "slug": "programming-languages",
          "source": "db"
        },
        "dimension_id": 1,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 15,
            "rationale": null,
            "role_archetype": null,
            "slug": "full-stack-engineer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 435,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "fullstack-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 3,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages \u0026 DSLs",
          "id": 475,
          "rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
          "slug": "programming-languages-dsls",
          "source": "db"
        },
        "dimension_id": 475,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Engineering Manager",
            "id": 121,
            "rationale": null,
            "role_archetype": null,
            "slug": "engineering-manager",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 3,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages and Scripting",
          "id": 59,
          "rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
          "slug": "programming-languages-and-scripting",
          "source": "db"
        },
        "dimension_id": 59,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cyber Security Engineer",
            "id": 5,
            "rationale": null,
            "role_archetype": null,
            "slug": "cybersecurity-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 3,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for Data Work",
          "id": 21,
          "rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
          "slug": "programming-languages-for-data-work",
          "source": "db"
        },
        "dimension_id": 21,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Data Engineer",
            "id": 2,
            "rationale": null,
            "role_archetype": null,
            "slug": "data-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 3,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for ML Systems",
          "id": 39,
          "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
          "slug": "programming-languages-for-ml-systems",
          "source": "db"
        },
        "dimension_id": 39,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          },
          {
            "display_name": "MLOps Engineer",
            "id": 16,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 3,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages for XR",
          "id": 97,
          "rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
          "slug": "programming-languages-for-xr",
          "source": "db"
        },
        "dimension_id": 97,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "AR/VR Engineer",
            "id": 8,
            "rationale": null,
            "role_archetype": null,
            "slug": "ar-vr-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 3,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Python Programming",
          "id": 290,
          "rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
          "slug": "python-programming",
          "source": "db"
        },
        "dimension_id": 290,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 3,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "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": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "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": 3,
        "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
      }
    ],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 0
  },
  "planner_output": null,
  "run_id": "3bb0bfe3-c4a9-4f10-94b2-b5661d916a21"
}

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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