Pipeline run
d6e99879-554f-46bd-a88f-a7165b1e127c
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
AI Engineer
CASE Aslug: ai-engineer · id: 13 · source: db
Multi-alias tie (3 roles at 1.0) resolved by TIER_A_KRA: AI Engineer
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Who are we? Dataflo is developing a groundbreaking new product in the latest AI space tailored for enterprise customers. Our new product focuses on assisting finance departments within enterprise companies, aiming to simplify and streamline their operations involving finance data and documents. We are seeking a talented Associate Software Engineer with a focus on Python and AI to join our dynamic team and contribute to the development of this innovative SaaS solution. Impact and Ownership: As an Associate Software Engineer, you will have significant impact and ownership over the development of our AI-driven SaaS product. Your contributions will directly influence the quality, performance, and scalability of the solutions we provide to enterprise finance departments. You will take ownership of key features and modules, ensuring they are delivered on time and meet the high standards our customers expect. Key Responsibilities: • Utilize Python to build and sustain software applications with a strong focus on AI and machine learning. • Ensure performance, usability, and scalability in AI applications by leveraging advanced Python techniques. • Identify and resolve issues to maintain low latency and high availability in AI systems. • Participate in and conduct code reviews, providing constructive feedback on software design and architecture. • Work with cross-functional teams to define project requirements and scope, ensuring alignment with AI objectives. • Apply your expertise in AI libraries and frameworks like TensorFlow, PyTorch, or similar tools. • Work with Large Language Models such as GPT-4, Llama, and vectorstores. • Integrate Python applications with databases, ensuring efficient data storage and retrieval. • Utilize Amazon Web Services (AWS) for deploying and managing cloud-based AI applications. • Utilize robust analytical and problem-solving abilities to tackle complex AI challenges. • Exhibit excellent communication and teamwork skills to collaborate effectively within the team and with stakeholders. Key Requirements: • Degree in Computer Science, Engineering, or a related field. • Minimum 4 years of relevant experience and maximum 6 years of overall experience. • Proven experience as a Python Developer, with a focus on AI and machine learning projects. • Strong knowledge of Django, Flask, or similar Python frameworks, with an emphasis on AI integration. • Proficient in integrating Python applications with databases. • Experience with Amazon Web Services (AWS) for cloud-based solutions. • Familiarity with large language model (LLM) frameworks for AI development. • Familiarity in concepts such as data chunking, embedding, and various similarity search approaches like cosine similarity. Why Join Us? • Be part of a team that is working on cutting-edge technology products in the AI and SaaS space. • Experience high growth potential within a pioneering company. • Engage in a challenging environment where you solve interesting problems every day. • Work on innovative products that have a real impact on enterprise customers. • Collaborate with a talented and diverse team of experts in the field. • Enjoy a flexible work environment with ample opportunities for growth and development. • Receive a competitive compensation and benefits package. Benefits: • 3L health insurance for the employee and immediate family • 10L Term insurance for the employee • Free full body health checkup once a year Note: This is a work-from-office role based in Perungudi, Chennai
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
- AI (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Artificial Intelligence
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: AI appears in a large and growing share of job descriptions across software, data, and product roles; major vendors like Microsoft, Google, and AWS have broad AI offerings and hiring demand reflects mainstream adoption.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1020
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
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 saved |
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- TensorFlow (CANONICAL) primary
- TF1 (VERSION)
- TF2 (VERSION)
- TensorFlow 1 (VERSION)
- TensorFlow 1.x (VERSION)
- TensorFlow 2 (VERSION)
- TensorFlow 2.x (VERSION)
- tensorflow 1 (VERSION)
- tensorflow 1.x (VERSION)
- tensorflow 2 (VERSION)
- tensorflow 2.x (VERSION)
- tensorflow v1 (VERSION)
- tensorflow v2 (VERSION)
- tf (VERSION)
- tf1 (VERSION)
- tf2 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Library
- Sub-category
- Machine Learning Library
- Vendor
- License
- apache_2
- Year introduced
- 2015
- Confidence
- 0.90
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 2.x
Maturity reasoning: TensorFlow appears in many ML/AI job descriptions and remains a standard production framework, with strong GitHub activity and broad vendor support from Google and cloud platforms.
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) |
Aliases — catalog
- PyTorch (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Library
- Sub-category
- Machine Learning Library
- Vendor
- Meta
- License
- bsd
- Year introduced
- 2016
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: PyTorch appears in a large volume of ML/AI job descriptions and is a standard framework in research and production, alongside TensorFlow and CUDA ecosystems.
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
-
Model Fine-Tuning & Adaptation Catalog dimension db id 212
Library dimension (catalog)
Roles linked in library: AI 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) |
|
Model Fine-Tuning & Adaptation
model-fine-tuning-adaptation
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
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
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- FAST
- Typical lifespan
- SHORT_LIVED
- Version strategy
- VERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- FAST
- Typical lifespan
- SHORT_LIVED
- Version strategy
- VERSIONED
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
- FAST
- Typical lifespan
- SHORT_LIVED
- Version strategy
- VERSIONED
Aliases — catalog
- AWS (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Platform
- Sub-category
- Cloud Platform
- Vendor
- Amazon
- License
- other_open
- Year introduced
- 2006
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: AWS is a hiring-pipeline staple: it appears in a large share of cloud/DevOps job descriptions and dominates public cloud market share, with broad certification and vendor ecosystem support.
Skill profile (library / DB)
- Skill nature
- PLATFORM
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 9
- Sub-category id
- 46
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Platforms Catalog dimension db id 20
Library dimension (catalog)
Roles linked in library: .NET Backend Developer, Backend Developer, Cyber Security Engineer, Data Engineer, DevOps Engineer, Fullstack Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, ML Engineer, MLOps Engineer, Node.js Backend Developer, Python Backend Developer, Scala Backend Developer
-
Cloud Platforms for AI Deployment Catalog dimension db id 211
Library dimension (catalog)
Roles linked in library: AI Engineer
-
Cloud Provider Platforms Catalog dimension db id 131
Library dimension (catalog)
Roles linked in library: Cloud Architect, Cloud Security Engineer
-
Cloud Security Posture Tools Catalog dimension db id 64
Library dimension (catalog)
Roles linked in library: Cloud Security Engineer, Cyber Security Engineer
-
Vendor Product Families Catalog dimension db id 477
Library dimension (catalog)
Roles linked in library: Engineering Manager
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Platforms
cloud-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
Cloud Provider Platforms
cloud-provider-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Cloud Security Posture Tools
cloud-security-posture-tools
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Vendor Product Families
vendor-product-families
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Django (CANONICAL) primary
- Django 1 (VERSION)
- Django 1.x (VERSION)
- Django 2 (VERSION)
- Django 2.x (VERSION)
- Django 3 (VERSION)
- Django 3.x (VERSION)
- Django 4 (VERSION)
- Django 4.x (VERSION)
- Django 5 (VERSION)
- Django 5.x (VERSION)
- Django1 (VERSION)
- Django2 (VERSION)
- Django3 (VERSION)
- Django4 (VERSION)
- Django5 (VERSION)
- django 2 (VERSION)
- django 2.x (VERSION)
- django 3 (VERSION)
- django 3.x (VERSION)
- django 4 (VERSION)
- django 4.x (VERSION)
- django 5 (VERSION)
- django 5.0 (VERSION)
- django 5.x (VERSION)
- django2 (VERSION)
- django2.x (VERSION)
- django3 (VERSION)
- django3.x (VERSION)
- django4 (VERSION)
- django4.x (VERSION)
- django5 (VERSION)
- django5.0 (VERSION)
- django5.x (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Web Framework
- Vendor
- Django Software Foundation
- License
- bsd
- Year introduced
- 2005
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 5
Maturity reasoning: Django appears in many backend web job descriptions and remains a standard Python web framework; its GitHub ecosystem and long-term LTS releases show sustained market demand.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 35
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Frameworks & Libraries Catalog dimension db id 360
Library dimension (catalog)
Roles linked in library: Drupal Dev, Engineering Manager
-
Web Application Frameworks Catalog dimension db id 2
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer, Java Backend Developer, Node.js Backend Developer, PHP Backend Developer, Python Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Frameworks & Libraries
frameworks-libraries
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Web Application Frameworks
web-application-frameworks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Flask (CANONICAL) primary
- flask 2 (VERSION)
- flask 2.x (VERSION)
- flask 3 (VERSION)
- flask 3.x (VERSION)
- flask2 (VERSION)
- flask3 (VERSION)
- flask>=3 (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Web Framework
- Vendor
- Pallets Projects
- License
- bsd
- Year introduced
- 2010
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- 3.x
Maturity reasoning: Flask appears in many Python web developer job postings and remains a common lightweight framework in hiring pipelines, though often alongside Django/FastAPI rather than as a niche tool.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 35
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
-
Web Application Frameworks Catalog dimension db id 2
Library dimension (catalog)
Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer, Java Backend Developer, Node.js Backend Developer, PHP Backend Developer, Python Backend Developer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Web Application Frameworks
web-application-frameworks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- TOOL
- Volatility
- FAST
- Typical lifespan
- SHORT_LIVED
- Version strategy
- VERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- PRACTICE
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Machine Learning Frameworks
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Version strategy
- UNVERSIONED
All API 3 persistence rows
Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.
| Skill | Tag | Dimension | Skill↔dim | Role↔dim | Outcome | Notes |
|---|---|---|---|---|---|---|
| Python | in_db |
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages
programming-languages
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages & DSLs
programming-languages-dsls
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages and Scripting
programming-languages-and-scripting
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for Data Work
programming-languages-for-data-work
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Python | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension 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) | |
| AI | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Machine Learning | in_db |
AI Governance and Model Security
ai-governance-and-model-security
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Machine Learning | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| TensorFlow | in_db |
ML Frameworks and Libraries
ml-frameworks-and-libraries
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| PyTorch | in_db |
ML Frameworks and Libraries
ml-frameworks-and-libraries
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| PyTorch | in_db |
Model Fine-Tuning & Adaptation
model-fine-tuning-adaptation
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| AWS | in_db |
Cloud Platforms
cloud-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| AWS | in_db |
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| AWS | in_db |
Cloud Provider Platforms
cloud-provider-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| AWS | in_db |
Cloud Security Posture Tools
cloud-security-posture-tools
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| AWS | in_db |
Vendor Product Families
vendor-product-families
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Django | in_db |
Frameworks & Libraries
frameworks-libraries
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Django | in_db |
Web Application Frameworks
web-application-frameworks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Flask | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Flask | in_db |
Web Application Frameworks
web-application-frameworks
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | Large Language Models | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | GPT-4 | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=SHORT_LIVED | |
| canonical_skill_proposed | Llama | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=SHORT_LIVED | |
| canonical_skill_proposed | vectorstores | type=Data Engineering Tools subtype=general nature=TOOL lifespan=SHORT_LIVED | |
| canonical_skill_proposed | LLM frameworks | type=Machine Learning Frameworks subtype=general nature=TOOL lifespan=SHORT_LIVED | |
| canonical_skill_proposed | data chunking | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR | |
| canonical_skill_proposed | embedding | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=MULTI_YEAR | |
| canonical_skill_proposed | cosine similarity | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=EVERGREEN |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Dataflo is developing a groundbreaking",
"last_5_words": "operations involving finance data and documents."
},
"text": "Dataflo is developing a groundbreaking new product in the latest AI space tailored for enterprise customers. Our new product focuses on assisting finance departments within enterprise companies, aiming to simplify and streamline their operations involving finance data and documents.",
"word_count": 43
},
"certifications": [],
"company_name": "Dataflo",
"ctc": null,
"domain": {
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"aliases": [
"SaaS",
"AI Products"
],
"domain": "Software \u0026 SaaS Products"
},
"secondary": null
},
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{
"level": "Bachelor\u0027s",
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"raw": "Degree in Computer Science, Engineering, or a related field.",
"requirement": "required"
}
],
"experience": {
"max": 6,
"min": 4,
"raw": "Minimum 4 years of relevant experience and maximum 6 years of overall experience."
},
"job_locations": [
{
"aliases": [
"Perungudi"
],
"city": "Chennai",
"country": "India",
"state": "Tamil Nadu",
"work_mode": "onsite"
}
],
"role": "Associate Software Engineer",
"role_aliases": [
"Software Engineer",
"Python Developer",
"AI Engineer"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 11,
"heading": "Key Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Utilize Python to build",
"last_5_words": "with the team and stakeholders."
},
"text": "\u2022 Utilize Python to build and sustain software applications with a strong focus on AI and machine learning.\n\u2022 Ensure performance, usability, and scalability in AI applications by leveraging advanced Python techniques.\n\u2022 Identify and resolve issues to maintain low latency and high availability in AI systems.\n\u2022 Participate in and conduct code reviews, providing constructive feedback on software design and architecture.\n\u2022 Work with cross-functional teams to define project requirements and scope, ensuring alignment with AI objectives.\n\u2022 Apply your expertise in AI libraries and frameworks like TensorFlow, PyTorch, or similar tools.\n\u2022 Work with Large Language Models such as GPT-4, Llama, and vectorstores.\n\u2022 Integrate Python applications with databases, ensuring efficient data storage and retrieval.\n\u2022 Utilize Amazon Web Services (AWS) for deploying and managing cloud-based AI applications.\n\u2022 Utilize robust analytical and problem-solving abilities to tackle complex AI challenges.\n\u2022 Exhibit excellent communication and teamwork skills to collaborate effectively within the team and with stakeholders.",
"word_count": 211
},
{
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"heading": "Key Requirements",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Degree in Computer Science,",
"last_5_words": "similarity search approaches like cosine."
},
"text": "\u2022 Degree in Computer Science, Engineering, or a related field.\n\u2022 Minimum 4 years of relevant experience and maximum 6 years of overall experience.\n\u2022 Proven experience as a Python Developer, with a focus on AI and machine learning projects.\n\u2022 Strong knowledge of Django, Flask, or similar Python frameworks, with an emphasis on AI integration.\n\u2022 Proficient in integrating Python applications with databases.\n\u2022 Experience with Amazon Web Services (AWS) for cloud-based solutions.\n\u2022 Familiarity with large language model (LLM) frameworks for AI development.\n\u2022 Familiarity in concepts such as data chunking, embedding, and various similarity search approaches like cosine similarity.",
"word_count": 88
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Python"
},
{
"is_primary": true,
"skill_name": "AI"
},
{
"is_primary": true,
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},
{
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},
{
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},
{
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},
{
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"skill_name": "GPT-4"
},
{
"is_primary": true,
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},
{
"is_primary": false,
"skill_name": "vectorstores"
},
{
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},
{
"is_primary": true,
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{
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"skill_name": "Flask"
},
{
"is_primary": false,
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},
{
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},
{
"is_primary": false,
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},
{
"is_primary": false,
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}
],
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},
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"about_company": {
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},
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}
],
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},
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{
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],
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}
],
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],
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{
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"source_marker": {
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},
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},
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"word_count": 88
}
],
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},
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"run_id": "d6e99879-554f-46bd-a88f-a7165b1e127c",
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{
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{
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{
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}
],
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{
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{
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},
{
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{
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],
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},
{
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{
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},
{
"kra_text": "Defines AI governance frameworks including fairness standards, transparency obligations, explainability requirements, and human oversight accountability structures.",
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},
{
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],
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},
{
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},
{
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{
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{
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{
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{
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],
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{
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{
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{
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]
},
"stage4_decision": {
"alias_collision_detected": true,
"case": "A",
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"matched_dimensions": [],
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"queued": false,
"reasoning": "Multi-alias tie (3 roles at 1.0) resolved by TIER_A_KRA: AI Engineer",
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},
"stage5_updates": null
}
API 2 — extract-details
{
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"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
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"typical_lifespan": "EVERGREEN",
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},
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},
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"existing_alias_text": "PyTorch",
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"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": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "DevOps Engineer",
"id": 10,
"rationale": null,
"role_archetype": null,
"slug": "devops-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-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": "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"
},
{
"display_name": "Node.js Backend Developer",
"id": 82,
"rationale": null,
"role_archetype": "Engineering",
"slug": "node-backend-developer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "Scala Backend Developer",
"id": 87,
"rationale": null,
"role_archetype": "Engineering",
"slug": "scala-backend-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Platforms for AI Deployment",
"id": 211,
"rationale": "Major cloud services that provide infrastructure and managed services for AI workloads.",
"slug": "cloud-platforms-for-ai-deployment",
"source": "db"
},
"input_skill": "AWS",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Provider Platforms",
"id": 131,
"rationale": "Major cloud platforms and their core service ecosystems used to design target-state architectures, choose deployment boundaries, and evaluate managed capabilities. This is the primary substrate for cloud architecture decisions.",
"slug": "cloud-provider-platforms",
"source": "db"
},
"input_skill": "AWS",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Architect",
"id": 9,
"rationale": null,
"role_archetype": null,
"slug": "cloud-architect",
"source": "db"
},
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Posture Tools",
"id": 64,
"rationale": "Cloud-native security platforms used to assess misconfiguration, workload exposure, and cloud control coverage. This dimension includes the major CNAPP/CSPM/CWPP vendors and cloud security services the role reviews and tunes.",
"slug": "cloud-security-posture-tools",
"source": "db"
},
"input_skill": "AWS",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Vendor Product Families",
"id": 477,
"rationale": "Coordinate usage, licensing, and architecture decisions for major vendor software and cloud product families.",
"slug": "vendor-product-families",
"source": "db"
},
"input_skill": "AWS",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
]
}
],
"input_skill": "AWS",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Django",
"alias_type": "CANONICAL",
"id": 82,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django 1",
"alias_type": "VERSION",
"id": 83,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django 1.x",
"alias_type": "VERSION",
"id": 88,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django 2",
"alias_type": "VERSION",
"id": 84,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django 2.x",
"alias_type": "VERSION",
"id": 89,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django 3",
"alias_type": "VERSION",
"id": 85,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django 3.x",
"alias_type": "VERSION",
"id": 90,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django 4",
"alias_type": "VERSION",
"id": 86,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django 4.x",
"alias_type": "VERSION",
"id": 91,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django 5",
"alias_type": "VERSION",
"id": 87,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django 5.x",
"alias_type": "VERSION",
"id": 92,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django1",
"alias_type": "VERSION",
"id": 2285,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django2",
"alias_type": "VERSION",
"id": 2286,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django3",
"alias_type": "VERSION",
"id": 2287,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django4",
"alias_type": "VERSION",
"id": 2288,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Django5",
"alias_type": "VERSION",
"id": 2289,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django 2",
"alias_type": "VERSION",
"id": 6523,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django 2.x",
"alias_type": "VERSION",
"id": 6531,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django 3",
"alias_type": "VERSION",
"id": 6524,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django 3.x",
"alias_type": "VERSION",
"id": 6532,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django 4",
"alias_type": "VERSION",
"id": 6525,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django 4.x",
"alias_type": "VERSION",
"id": 6533,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django 5",
"alias_type": "VERSION",
"id": 3569,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django 5.0",
"alias_type": "VERSION",
"id": 3572,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django 5.x",
"alias_type": "VERSION",
"id": 3573,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django2",
"alias_type": "VERSION",
"id": 6519,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django2.x",
"alias_type": "VERSION",
"id": 6527,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django3",
"alias_type": "VERSION",
"id": 6520,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django3.x",
"alias_type": "VERSION",
"id": 6528,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django4",
"alias_type": "VERSION",
"id": 6521,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django4.x",
"alias_type": "VERSION",
"id": 6529,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django5",
"alias_type": "VERSION",
"id": 3568,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django5.0",
"alias_type": "VERSION",
"id": 3570,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "django5.x",
"alias_type": "VERSION",
"id": 3571,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 5,
"display_name": "Django",
"id": 9,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "django",
"sub_category_id": 35,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Frameworks \u0026 Libraries",
"id": 360,
"rationale": "Manage adoption, integration, and best practices around key software frameworks and libraries.",
"slug": "frameworks-libraries",
"source": "db"
},
"input_skill": "Django",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Drupal Dev",
"id": 228,
"rationale": null,
"role_archetype": "Engineering",
"slug": "drupal-dev",
"source": "db"
},
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Web Application Frameworks",
"id": 2,
"rationale": "Server frameworks and runtimes used to build HTTP services, controllers, middleware, and request pipelines. These frameworks shape how backend endpoints are structured and delivered.",
"slug": "web-application-frameworks",
"source": "db"
},
"input_skill": "Django",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 435,
"rationale": null,
"role_archetype": "Engineering",
"slug": "fullstack-developer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-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"
}
]
}
],
"input_skill": "Django",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Flask",
"alias_type": "CANONICAL",
"id": 1980,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "flask 2",
"alias_type": "VERSION",
"id": 1985,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "flask 2.x",
"alias_type": "VERSION",
"id": 1987,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "flask 3",
"alias_type": "VERSION",
"id": 1982,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "flask 3.x",
"alias_type": "VERSION",
"id": 1983,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "flask2",
"alias_type": "VERSION",
"id": 1986,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "flask3",
"alias_type": "VERSION",
"id": 1981,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "flask\u003e=3",
"alias_type": "VERSION",
"id": 1984,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 5,
"display_name": "Flask",
"id": 1344,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "flask",
"sub_category_id": 35,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "Flask",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Web Application Frameworks",
"id": 2,
"rationale": "Server frameworks and runtimes used to build HTTP services, controllers, middleware, and request pipelines. These frameworks shape how backend endpoints are structured and delivered.",
"slug": "web-application-frameworks",
"source": "db"
},
"input_skill": "Flask",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 435,
"rationale": null,
"role_archetype": "Engineering",
"slug": "fullstack-developer",
"source": "db"
},
{
"display_name": "Java Backend Developer",
"id": 79,
"rationale": null,
"role_archetype": "Engineering",
"slug": "java-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"
}
]
}
],
"input_skill": "Flask",
"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": "LLM frameworks",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Machine Learning Frameworks",
"skill_nature": "TOOL",
"sub_category": "general",
"typical_lifespan": "SHORT_LIVED",
"version_strategy": "VERSIONED",
"volatility": "FAST"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "llm-frameworks",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "data chunking",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "PRACTICE",
"sub_category": "general",
"typical_lifespan": "MULTI_YEAR",
"version_strategy": "UNVERSIONED",
"volatility": "MEDIUM"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "data-chunking",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "embedding",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Machine Learning Frameworks",
"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": "embedding",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [],
"canonical": null,
"dimensions": [],
"input_skill": "cosine similarity",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Machine Learning Frameworks",
"skill_nature": "CONCEPT",
"sub_category": "general",
"typical_lifespan": "EVERGREEN",
"version_strategy": "UNVERSIONED",
"volatility": "STABLE"
},
"enrichment": null,
"keep_log": [],
"locked_dimensions": [],
"merge_log": [],
"placed": null,
"relationships": null,
"skill_id": "cosine-similarity",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"Large Language Models",
"GPT-4",
"Llama",
"vectorstores",
"LLM frameworks",
"data chunking",
"embedding",
"cosine similarity"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "AI Engineer",
"id": 13,
"rationale": "Multi-alias tie (3 roles at 1.0) resolved by TIER_A_KRA: AI Engineer",
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Python",
"tag": "in_db"
},
{
"skill": "AI",
"tag": "in_db"
},
{
"skill": "Machine Learning",
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LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.