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Pipeline run

aa8477cc-e327-4187-b406-c0eabba9b392

Pipeline LLM cost (USD)
API 1: $0.0034 API 2: $0.0003 API 3: $0.0000 Total: $0.0037

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work · AI with ML
Build and tune ML/deep learning models end-to-end: choose algorithms, find data, define validation, feature engineering and augmentation, then train, debug, and deploy models with Python/scikit-learn/pandas.
"Training models and tuning their hyperparameters"
Tech stack maturity
Mainstream Modern
The role centers on Python and scikit-learn with standard machine-learning practices like data augmentation and model validation, which are typical of a modern but not bleeding-edge stack.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
1.70 / 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): AI, ML, AI/ML, Machine Learning, Deep Learning, Artificial Intelligence
Evidence — skills matched in JD (12)
Python scikit-learn pandas Machine Learning Deep Learning Data Augmentation Feature Engineering Model Training Hyperparameter Tuning Model Validation Model Deployment Big Data
Skill cluster (3 dimension groups, role-scoped)
AI Governance and Model Security
Machine Learning
Python Programming
Python
Cross-cutting / unaligned
scikit-learn pandas Deep Learning Data Augmentation Feature Engineering Model Training Hyperparameter Tuning Model Validation Model Deployment Big Data
Show KRA description ↓
Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability Finding available datasets that could be used for training Defining validation strategies Defining the preprocessing or feature engineering to be done on a given dataset Defining data augmentation pipelines Training models and tuning their hyperparameters Analyzing the errors of the model and designing strategies to overcome them Deploying models to production Proficiency with a deep learning framework with required algorithms and tools in the market Proficiency with AI and one or more tools available in the market Proficiency with Python and libraries for machine learning such as scikit-learn and pandas Expertise in visualizing and manipulating big datasets AI with ML Latest tools available in the market for AI, required python libraries for ML

Signals

Skill ml-engineer
0.36
Alias ml-engineer
1.00
KRA ml-engineer
0.55
Status: completed Created: 2026-05-27T17:34:50.967245Z Updated: 2026-05-27T17:35:51.960477Z API 3 duration: 5515 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

Data Scientist

CASE A

slug: data-scientist · id: 49 · source: db

Multi-alias tie (3 roles at 1.0) resolved by TIER_B_TITLE: Data Scientist

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

Role:
Experience: 10-14+ years
Location: Bangalore,Chennai and Vijaywada
WORK Model: WFO
Notice Period: Immediate Joiner to serving notice only


JD:
Familiarity to AI OPS, and skilled in AI and machine learning to understand/analyze and extract value from all sort of network data. Be conversant one or more AI/ML tools. Should be passionate about artificial intelligence and stay up-to-date with the latest developments in the field. Process the logs of security products and derive meaningful output for various use cases, and Correlate the events and derive actions.

Responsibilities
Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress
Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability
Finding available datasets that could be used for training
Defining validation strategies
Defining the preprocessing or feature engineering to be done on a given dataset
Defining data augmentation pipelines
Training models and tuning their hyperparameters
Analyzing the errors of the model and designing strategies to overcome them
Deploying models to production


Skills
Proficiency with a deep learning framework with required algorithms and tools in the market
Proficiency with AI and one or more tools available in the market
Proficiency with Python and libraries for machine learning such as scikit-learn and pandas
Expertise in visualizing and manipulating big datasets


Mandatory SKills
AI with ML
Latest tools available in the market for AI, required python libraries for ML

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 skipped (dimension not under chosen role)
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)
scikit-learn Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: scikit-learn id=197 · scikit-learn

Aliases — catalog

  • scikit-learn (CANONICAL) primary

Context tags (catalog)

GridSearchCV K-fold NumPy Pandas Pipeline SVM classification clustering cross-validation cross_validation data_preprocessing ensemble_methods feature engineering feature_importance hyperparameter_tuning imbalanced-learn joblib logistic regression metrics model_selection pipelines predictive_modeling preprocessing random forest regression scoring_metrics train_test_split

Stored enrichment (catalog DB)

Category
Library
Sub-category
Machine Learning Library
Vendor
scikit-learn developers
License
bsd
Year introduced
2007
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: Commonly listed in ML/data science job descriptions and widely used in production Python ML stacks; no vendor sunset or replacement signal, and GitHub activity remains strong.

Skill profile (library / DB)

Skill nature
LIBRARY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
7
Sub-category id
156
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ML Frameworks and Libraries Catalog dimension db id 40

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ML Frameworks and Libraries
ml-frameworks-and-libraries
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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
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)
Deep Learning 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
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Data Augmentation Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: data augmentation id=258 · data-augmentation

Aliases — catalog

  • data augmentation (CANONICAL) primary

Context tags (catalog)

GANs SMOTE back-translation cutout flip image transforms jitter mixup noise injection oversampling paraphrasing random crop rotation synthetic data token masking

Stored enrichment (catalog DB)

Category
Methodology
Sub-category
Data Preparation Methodology
Confidence
0.95
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common in ML job descriptions and standard practice in vision/NLP pipelines; widely supported in libraries like Albumentations and torchvision, with strong GitHub usage and tutorial volume.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
159
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Data Preparation and Labeling Catalog dimension db id 52

    Library dimension (catalog)

    Roles linked in library: ML Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Data Preparation and Labeling
data-preparation-and-labeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Feature Engineering 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
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Model Training 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
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Hyperparameter Tuning 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
Model Validation Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: model validation id=3256 · model-validation

Aliases — catalog

  • model validation (CANONICAL) primary

Context tags (catalog)

AUC F1 score ROC curve confusion matrix cross-validation data leakage holdout set hyperparameter tuning model robustness overfitting precision recall test dataset underfitting validation metrics

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Model Validation
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: Model validation is a standard ML/DS hiring topic; JDs commonly ask for cross-validation, holdout testing, and metrics-based evaluation, and major frameworks like scikit-learn and TensorFlow document it as core practice.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
2499
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Data Validation and Error Handling Catalog dimension db id 15

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, PHP Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Data Validation and Error Handling
data-validation-and-error-handling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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
Big Data Secondary 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

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 skipped (dimension not under chosen role)
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)
scikit-learn in_db
ML Frameworks and Libraries
ml-frameworks-and-libraries
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 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)
Data Augmentation in_db
Data Preparation and Labeling
data-preparation-and-labeling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Model Validation in_db
Data Validation and Error Handling
data-validation-and-error-handling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed pandas | type=Data Engineering Tools subtype=general nature=TOOL lifespan=MULTI_YEAR
canonical_skill_proposed Deep Learning | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Feature Engineering | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=EVERGREEN
canonical_skill_proposed Model Training | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=EVERGREEN
canonical_skill_proposed Hyperparameter Tuning | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Model Deployment | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Big Data | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
nano JD Parser — gpt-4.1-nano click to toggle
RoleAI/ML Engineer
Experience10-14+ years
DomainIT Services & Consulting
Location Bangalore, India (onsite)
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [],
  "company_name": null,
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [],
      "domain": "IT Services \u0026 Consulting"
    },
    "secondary": null
  },
  "education": [],
  "experience": {
    "max": 14,
    "min": 10,
    "raw": "10-14+ years"
  },
  "job_locations": [
    {
      "aliases": [
        "Bengaluru"
      ],
      "city": "Bangalore",
      "country": "India",
      "state": null,
      "work_mode": "onsite"
    },
    {
      "aliases": [],
      "city": "Chennai",
      "country": "India",
      "state": null,
      "work_mode": "onsite"
    },
    {
      "aliases": [],
      "city": "Vijaywada",
      "country": "India",
      "state": null,
      "work_mode": "onsite"
    }
  ],
  "role": "AI/ML Engineer",
  "role_aliases": [
    "Machine Learning Engineer",
    "Data Scientist",
    "AI Engineer"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 9,
      "heading": "Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Understanding business objectives and developing",
        "last_5_words": "models to production"
      },
      "text": "Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress\nAnalyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability\nFinding available datasets that could be used for training\nDefining validation strategies\nDefining the preprocessing or feature engineering to be done on a given dataset\nDefining data augmentation pipelines\nTraining models and tuning their hyperparameters\nAnalyzing the errors of the model and designing strategies to overcome them\nDeploying models to production",
      "word_count": 104
    },
    {
      "bullet_count": 4,
      "heading": "Skills",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Proficiency with a deep learning",
        "last_5_words": "visualizing and manipulating big datasets"
      },
      "text": "Proficiency with a deep learning framework with required algorithms and tools in the market\nProficiency with AI and one or more tools available in the market\nProficiency with Python and libraries for machine learning such as scikit-learn and pandas\nExpertise in visualizing and manipulating big datasets",
      "word_count": 56
    },
    {
      "bullet_count": 2,
      "heading": "Mandatory SKills",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "AI with ML\nLatest tools available",
        "last_5_words": "for AI, required python libraries"
      },
      "text": "AI with ML\nLatest tools available in the market for AI, required python libraries for ML",
      "word_count": 19
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Python"
    },
    {
      "is_primary": true,
      "skill_name": "scikit-learn"
    },
    {
      "is_primary": true,
      "skill_name": "pandas"
    },
    {
      "is_primary": true,
      "skill_name": "Machine Learning"
    },
    {
      "is_primary": true,
      "skill_name": "Deep Learning"
    },
    {
      "is_primary": true,
      "skill_name": "Data Augmentation"
    },
    {
      "is_primary": true,
      "skill_name": "Feature Engineering"
    },
    {
      "is_primary": true,
      "skill_name": "Model Training"
    },
    {
      "is_primary": true,
      "skill_name": "Hyperparameter Tuning"
    },
    {
      "is_primary": true,
      "skill_name": "Model Validation"
    },
    {
      "is_primary": true,
      "skill_name": "Model Deployment"
    },
    {
      "is_primary": false,
      "skill_name": "Big Data"
    }
  ],
  "jd_role": {
    "display_name": "AI/ML Engineer",
    "rationale": null,
    "role_aliases": [
      "Machine Learning Engineer",
      "Data Scientist",
      "AI Engineer"
    ],
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": null,
    "certifications": [],
    "company_name": null,
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [],
        "domain": "IT Services \u0026 Consulting"
      },
      "secondary": null
    },
    "education": [],
    "experience": {
      "max": 14,
      "min": 10,
      "raw": "10-14+ years"
    },
    "job_locations": [
      {
        "aliases": [
          "Bengaluru"
        ],
        "city": "Bangalore",
        "country": "India",
        "state": null,
        "work_mode": "onsite"
      },
      {
        "aliases": [],
        "city": "Chennai",
        "country": "India",
        "state": null,
        "work_mode": "onsite"
      },
      {
        "aliases": [],
        "city": "Vijaywada",
        "country": "India",
        "state": null,
        "work_mode": "onsite"
      }
    ],
    "role": "AI/ML Engineer",
    "role_aliases": [
      "Machine Learning Engineer",
      "Data Scientist",
      "AI Engineer"
    ],
    "role_archetype": "Data",
    "roles_and_responsibilities": [
      {
        "bullet_count": 9,
        "heading": "Responsibilities",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Understanding business objectives and developing",
          "last_5_words": "models to production"
        },
        "text": "Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress\nAnalyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability\nFinding available datasets that could be used for training\nDefining validation strategies\nDefining the preprocessing or feature engineering to be done on a given dataset\nDefining data augmentation pipelines\nTraining models and tuning their hyperparameters\nAnalyzing the errors of the model and designing strategies to overcome them\nDeploying models to production",
        "word_count": 104
      },
      {
        "bullet_count": 4,
        "heading": "Skills",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Proficiency with a deep learning",
          "last_5_words": "visualizing and manipulating big datasets"
        },
        "text": "Proficiency with a deep learning framework with required algorithms and tools in the market\nProficiency with AI and one or more tools available in the market\nProficiency with Python and libraries for machine learning such as scikit-learn and pandas\nExpertise in visualizing and manipulating big datasets",
        "word_count": 56
      },
      {
        "bullet_count": 2,
        "heading": "Mandatory SKills",
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        "source_marker": {
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API 2 — extract-details
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              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Data Augmentation",
      "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": "Feature Engineering",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Machine Learning Frameworks",
          "skill_nature": "PRACTICE",
          "sub_category": "general",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "UNVERSIONED",
          "volatility": "STABLE"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "feature-engineering",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Model Training",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Machine Learning Frameworks",
          "skill_nature": "PRACTICE",
          "sub_category": "general",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "UNVERSIONED",
          "volatility": "STABLE"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "model-training",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Hyperparameter Tuning",
      "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": "hyperparameter-tuning",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "model validation",
          "alias_type": "CANONICAL",
          "id": 4805,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "model validation",
        "id": 3256,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "model-validation",
        "sub_category_id": 2499,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Data Validation and Error Handling",
            "id": 15,
            "rationale": "Guardrails that keep backend request processing correct and predictable. Includes input validation, domain rules, exception handling, error mapping, and response semantics.",
            "slug": "data-validation-and-error-handling",
            "source": "db"
          },
          "input_skill": "Model Validation",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "Go Backend Developer",
              "id": 81,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "go-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Java Backend Developer",
              "id": 79,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "java-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Kotlin Backend Developer",
              "id": 84,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "kotlin-server-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Node.js Backend Developer",
              "id": 82,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "node-backend-developer",
              "source": "db"
            },
            {
              "display_name": "PHP Backend Developer",
              "id": 86,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "php-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Python Backend Developer",
              "id": 80,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "python-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Ruby Backend Developer",
              "id": 85,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "ruby-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Model Validation",
      "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": "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": "Big Data",
      "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": "big-data",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "pandas",
    "Deep Learning",
    "Feature Engineering",
    "Model Training",
    "Hyperparameter Tuning",
    "Model Deployment",
    "Big Data"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Data Scientist",
    "id": 49,
    "rationale": "Multi-alias tie (3 roles at 1.0) resolved by TIER_B_TITLE: Data Scientist",
    "role_archetype": "Engineering",
    "slug": "data-scientist",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "scikit-learn",
      "tag": "in_db"
    },
    {
      "skill": "pandas",
      "tag": "new"
    },
    {
      "skill": "Machine Learning",
      "tag": "in_db"
    },
    {
      "skill": "Deep Learning",
      "tag": "new"
    },
    {
      "skill": "Data Augmentation",
      "tag": "in_db"
    },
    {
      "skill": "Feature Engineering",
      "tag": "new"
    },
    {
      "skill": "Model Training",
      "tag": "new"
    },
    {
      "skill": "Hyperparameter Tuning",
      "tag": "new"
    },
    {
      "skill": "Model Validation",
      "tag": "in_db"
    },
    {
      "skill": "Model Deployment",
      "tag": "new"
    },
    {
      "skill": "Big Data",
      "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": 49,
        "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": 49,
        "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": 435,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "fullstack-developer",
            "source": "db"
          },
          {
            "display_name": "Fullstack Developer",
            "id": 15,
            "rationale": null,
            "role_archetype": null,
            "slug": "full-stack-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 49,
        "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": 49,
        "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": 49,
        "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": 49,
        "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": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          },
          {
            "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": 49,
        "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": 49,
        "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": 49,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "ML Frameworks and Libraries",
          "id": 40,
          "rationale": "Core libraries used to define models, train them, run inference, and evaluate predictive performance. These frameworks shape how ML engineers express model architectures and training loops.",
          "slug": "ml-frameworks-and-libraries",
          "source": "db"
        },
        "dimension_id": 40,
        "input_skill": "scikit-learn",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          },
          {
            "display_name": "MLOps Engineer",
            "id": 16,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 197,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 49,
        "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": 49,
        "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": 49,
        "dimension": {
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