Pipeline run
aa8477cc-e327-4187-b406-c0eabba9b392
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
Data Scientist
CASE Aslug: 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.
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.
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)
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) |
Aliases — catalog
- scikit-learn (CANONICAL) primary
Context tags (catalog)
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) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- Machine Learning (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Machine Learning
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Machine Learning appears in large volumes of job descriptions across data, product, and platform roles, and major cloud vendors (AWS, Google Cloud, Azure) offer dedicated ML services and certifications, indicating broad adoption.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1024
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
AI Governance and Model Security Catalog dimension db id 50
Library dimension (catalog)
Roles linked in library: AI Engineer, ML Engineer, MLOps Engineer
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
AI Governance and Model Security
ai-governance-and-model-security
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- data augmentation (CANONICAL) primary
Context tags (catalog)
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) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Aliases — catalog
- model validation (CANONICAL) primary
Context tags (catalog)
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) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- 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
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",
"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": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "aa8477cc-e327-4187-b406-c0eabba9b392",
"stage3_signals": {
"alias_found": true,
"alias_match_roles": [
{
"display_name": "ML Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 3,
"score": 1.0,
"slug": "ml-engineer",
"total_count": null
},
{
"display_name": "AI Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 13,
"score": 1.0,
"slug": "ai-engineer",
"total_count": null
},
{
"display_name": "Data Scientist",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 49,
"score": 1.0,
"slug": "data-scientist",
"total_count": null
}
],
"kra_match_roles": [
{
"display_name": "ML Engineer",
"kra_matches": [
{
"kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
"sentence": "Defining the preprocessing or feature engineering to be done on a given dataset",
"similarity": 0.6172
},
{
"kra_text": "Supports offline experimentation, hyperparameter tuning, and online A/B experiments to improve model quality and investigate production performance issues.",
"sentence": "Training models and tuning their hyperparameters",
"similarity": 0.5407
},
{
"kra_text": "Translates product requirements into machine learning system specifications including feature definitions, model architecture choices, and success metric definitions.",
"sentence": "Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress",
"similarity": 0.4884
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 3,
"score": 0.5488,
"slug": "ml-engineer",
"total_count": null
},
{
"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": "Proficiency with AI and one or more tools available in the market",
"similarity": 0.4918
},
{
"kra_text": "Optimizes AI pipeline efficiency by tuning model selection, context window usage, prompt caching, and batching strategies to reduce cost and latency.",
"sentence": "Training models and tuning their hyperparameters",
"similarity": 0.48
},
{
"kra_text": "Designs and implements prompt engineering workflows, few-shot examples, chain-of-thought patterns, and structured output parsing for AI feature pipelines.",
"sentence": "Defining the preprocessing or feature engineering to be done on a given dataset",
"similarity": 0.4282
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 13,
"score": 0.4666,
"slug": "ai-engineer",
"total_count": null
},
{
"display_name": "Data Engineer",
"kra_matches": [
{
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API 2 — extract-details
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"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": {
"difficulty_hint": "well_known",
"display_name": "Data Preparation and Labeling",
"id": 52,
"rationale": "Preparing training examples, labels, and datasets for model development. This includes cleaning, sampling, augmentation, and managing annotation workflows that feed model training.",
"slug": "data-preparation-and-labeling",
"source": "db"
},
"dimension_id": 52,
"input_skill": "Data Augmentation",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 258,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 49,
"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"
},
"dimension_id": 15,
"input_skill": "Model Validation",
"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": ".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"
}
],
"skill_dimension_saved": true,
"skill_id": 3256,
"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": "aa8477cc-e327-4187-b406-c0eabba9b392"
}