Pipeline run
8826841a-c1ac-42d9-908e-a4f167d414ca
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
Captured for admin review
We are looking for a Software Engineer versed in AI to lead the integration and deployment of AI solutions at an enterprise level. You will be responsible for designing and implementing AI-driven syst…
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
LLM / GenAI Engineer
domain · AI / ML CASE DOMAINslug: llm-genai-engineer · id: 151 · source: db
Domain=AI / ML; The JD centers on enterprise integration and deployment of LLM/GenAI solutions with RAG, prompt engineering, and AI application implementation, which best matches an LLM/GenAI Engineer.
Matched skills
Matched dimensions
Matched KRAs
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Job Reference # 310530BR Job Type Full Time Your role We are looking for a Software Engineer versed in AI to lead the integration and deployment of AI solutions at an enterprise level. You will be responsible for designing and implementing AI-driven systems with a strong focus on scalability, security, and cost efficiency. You will also identify AI opportunities in our tech products & engineering processes and present these use case for prioritization. This role requires deep expertise in & hands-on experience with LLMs, AI architecture, security, and responsible AI practices to ensure robust and compliant AI deployments – along with a proven track record of deliveries into production. Your team You’ll be working in the KeyLink team in Pune. Our role is to provide cutting edge eBanking services to our professional wholesale clients globally. The application is currently in a phase of renewal and your skills are vital to the strategic direction and success of the future platform. Your expertise • bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field. • hands-on experience in implementing real-world AI use cases. • expertise in Python, R or any related language, including AI frameworks like as LangChain and Semantic Kernel. • an AI-first mentality to problem solving and ideation. • strong experience in AI/ML integration with LLMs and Generative AI, RAG methodologies. • understanding of data privacy considerations and ethical practices in AI development. • prompt engineering expertise, including advanced prompting techniques. • knowledge of version control systems (e.g. Git) and Agile development methodologies. About Us UBS is the world’s largest and the only truly global wealth manager. We operate through four business divisions: Global Wealth Management, Personal & Corporate Banking, Asset Management and the Investment Bank. Our global reach and the breadth of our expertise set us apart from our competitors.. We have a presence in all major financial centers in more than 50 countries. Join us At UBS, we embrace flexible ways of working when the role permits. We offer different working arrangements like part-time, job-sharing and hybrid (office and home) working. Our purpose-led culture and global infrastructure help us connect, collaborate, and work together in agile ways to meet all our business needs. From gaining new experiences in different roles to acquiring fresh knowledge and skills, we know that great work is never done alone. We know that it's our people, with their unique backgrounds, skills, experience levels and interests, who drive our ongoing success. Together we’re more than ourselves. Ready to be part of #teamUBS and make an impact? Disclaimer / Policy Statements UBS is an Equal Opportunity Employer. We respect and seek to empower each individual and support the diverse cultures, perspectives, skills and experiences within our workforce.
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
- R (VERSION)
- R 3 (VERSION)
- R 3.x (VERSION)
- R 4 (VERSION)
- R 4.0 (VERSION)
- R 4.1 (VERSION)
- R 4.2 (VERSION)
- R 4.3 (VERSION)
- R 4.4 (VERSION)
- R 4.x (VERSION)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Programming Language
- Vendor
- R Core Team
- License
- gpl_v2
- Year introduced
- 1993
- Confidence
- 0.99
- Version strategy
- SEPARATE_ENTITY
- Version tag
- R 4.x
Maturity reasoning: R appears in many data science, statistics, and analytics job postings, and CRAN remains active with broad package usage across academia and industry.
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)
-
Programming Languages for ML Systems Catalog dimension db id 39
Library dimension (catalog)
Roles linked in library: ML Engineer, MLOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- LangChain (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Llm Application Framework
- Vendor
- Harrison Chase
- License
- mit
- Year introduced
- 2022
- Confidence
- 0.97
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: LangChain appears in many recent AI/LLM job postings and is widely used in app prototypes, but it’s still not a universal hiring staple like React or AWS.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 146
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
LLM Operations and Orchestration Catalog dimension db id 49
Library dimension (catalog)
Roles linked in library: AI Engineer, ML Engineer, MLOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
LLM Operations and Orchestration
llm-operations-and-orchestration
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Semantic Kernel (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Llm Application Framework
- Vendor
- Microsoft
- License
- mit
- Year introduced
- 2023
- Confidence
- 0.95
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Appears in growing LLM-app job postings and Microsoft-backed docs, but JD volume is still far below Python/React-level staples; GitHub activity and ecosystem integrations are rising.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 146
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
LLM Operations and Orchestration Catalog dimension db id 49
Library dimension (catalog)
Roles linked in library: AI Engineer, ML Engineer, MLOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
LLM Operations and Orchestration
llm-operations-and-orchestration
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- LLMs (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Large Language Models
- Confidence
- 0.96
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: LLMs are increasingly listed in job descriptions for AI/ML and product roles, and major vendors (OpenAI, Anthropic, Google) are shipping APIs and platforms, but they are not yet universal across engineering hiring.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 903
- 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) |
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
- RAG (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Retrieval Augmented Generation
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: RAG appears in many recent AI/ML job descriptions and vendor docs, but it is still not a universal baseline skill like Python or SQL; market demand is rising fast rather than fully standardized.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 904
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Prompt engineering (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Prompt Engineering
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Prompt engineering is increasingly listed in AI/LLM job descriptions and vendor docs, but it’s still not a universal hiring staple like Python or AWS.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 914
- 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
- Git (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Tool
- Sub-category
- Version Control Tool
- Vendor
- Linus Torvalds
- License
- gpl_v2
- Year introduced
- 2005
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Git is a hiring-pipeline staple: it appears in the vast majority of software engineering job descriptions and is the default VCS on GitHub/GitLab/Bitbucket.
Skill profile (library / DB)
- Skill nature
- TOOL
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 13
- Sub-category id
- 730
- 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
- Agile (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Agile
- Confidence
- 0.99
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Agile appears in a large share of software job descriptions and is a standard hiring-pipeline requirement; Scrum/Kanban are commonly listed alongside it, showing broad market adoption.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 3594
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
-
Software Concepts, Patterns & Practices Catalog dimension db id 478
Library dimension (catalog)
Roles linked in library: Engineering Manager
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) |
|
Software Concepts, Patterns & Practices
software-concepts-patterns-practices
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
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) | |
| R | in_db |
Programming Languages for ML Systems
programming-languages-for-ml-systems
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| LangChain | in_db |
LLM Operations and Orchestration
llm-operations-and-orchestration
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Semantic Kernel | in_db |
LLM Operations and Orchestration
llm-operations-and-orchestration
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| LLMs | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| RAG | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Prompt Engineering | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Git | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Agile | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Agile | in_db |
Software Concepts, Patterns & Practices
software-concepts-patterns-practices
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | Generative AI | type=Machine Learning Frameworks 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": {
"source_marker": {
"first_5_words": "UBS is the world\u2019s largest",
"last_5_words": "in more than 50 countries."
},
"text": "UBS is the world\u2019s largest and the only truly global wealth manager. We operate through four business divisions: Global Wealth Management, Personal \u0026 Corporate Banking, Asset Management and the Investment Bank. Our global reach and the breadth of our expertise set us apart from our competitors..\n\nWe have a presence in all major financial centers in more than 50 countries.",
"word_count": 64
},
"certifications": [],
"company_name": "UBS",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"FinTech",
"Wealth Management"
],
"domain": "Financial Services"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE - Computer Science / Data Science / Artificial Intelligence (or related)",
"raw": "bachelor\u2019s degree in Computer Science, Data Science, Artificial Intelligence, or a related field.",
"requirement": "required"
}
],
"experience": {
"max": null,
"min": null,
"raw": null
},
"job_locations": [
{
"aliases": [
"Pune, MH"
],
"city": "Pune",
"country": "India",
"state": null,
"work_mode": "hybrid"
}
],
"role": "Software Engineer",
"role_aliases": [
"Software Developer",
"SWE",
"AI Engineer"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 0,
"heading": "Your role",
"heading_was_present": true,
"source_marker": {
"first_5_words": "We are looking for a",
"last_5_words": "deliveries into production."
},
"text": "We are looking for a Software Engineer versed in AI to lead the integration and deployment of AI solutions at an enterprise level. You will be responsible for designing and implementing AI-driven systems with a strong focus on scalability, security, and cost efficiency. You will also identify AI opportunities in our tech products \u0026 engineering processes and present these use case for prioritization.\n\nThis role requires deep expertise in \u0026 hands-on experience with LLMs, AI architecture, security, and responsible AI practices to ensure robust and compliant AI deployments \u2013 along with a proven track record of deliveries into production.",
"word_count": 102
},
{
"bullet_count": 7,
"heading": "Your expertise",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 hands-on experience in implementing",
"last_5_words": "and Agile development methodologies."
},
"text": "\u2022 hands-on experience in implementing real-world AI use cases.\n\u2022 expertise in Python, R or any related language, including AI frameworks like as LangChain and Semantic Kernel.\n\u2022 an AI-first mentality to problem solving and ideation.\n\u2022 strong experience in AI/ML integration with LLMs and Generative AI, RAG methodologies.\n\u2022 understanding of data privacy considerations and ethical practices in AI development.\n\u2022 prompt engineering expertise, including advanced prompting techniques.\n\u2022 knowledge of version control systems (e.g. Git) and Agile development methodologies.",
"word_count": 83
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Python"
},
{
"is_primary": true,
"skill_name": "R"
},
{
"is_primary": true,
"skill_name": "LangChain"
},
{
"is_primary": true,
"skill_name": "Semantic Kernel"
},
{
"is_primary": true,
"skill_name": "LLMs"
},
{
"is_primary": true,
"skill_name": "Generative AI"
},
{
"is_primary": true,
"skill_name": "RAG"
},
{
"is_primary": true,
"skill_name": "Prompt Engineering"
},
{
"is_primary": true,
"skill_name": "Git"
},
{
"is_primary": true,
"skill_name": "Agile"
}
],
"jd_role": {
"display_name": "Software Engineer",
"rationale": null,
"role_aliases": [
"Software Developer",
"SWE",
"AI Engineer"
],
"role_archetype": "Engineering",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "UBS is the world\u2019s largest",
"last_5_words": "in more than 50 countries."
},
"text": "UBS is the world\u2019s largest and the only truly global wealth manager. We operate through four business divisions: Global Wealth Management, Personal \u0026 Corporate Banking, Asset Management and the Investment Bank. Our global reach and the breadth of our expertise set us apart from our competitors..\n\nWe have a presence in all major financial centers in more than 50 countries.",
"word_count": 64
},
"certifications": [],
"company_name": "UBS",
"ctc": null,
"domain": {
"primary": {
"aliases": [
"FinTech",
"Wealth Management"
],
"domain": "Financial Services"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE - Computer Science / Data Science / Artificial Intelligence (or related)",
"raw": "bachelor\u2019s degree in Computer Science, Data Science, Artificial Intelligence, or a related field.",
"requirement": "required"
}
],
"experience": {
"max": null,
"min": null,
"raw": null
},
"job_locations": [
{
"aliases": [
"Pune, MH"
],
"city": "Pune",
"country": "India",
"state": null,
"work_mode": "hybrid"
}
],
"role": "Software Engineer",
"role_aliases": [
"Software Developer",
"SWE",
"AI Engineer"
],
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 0,
"heading": "Your role",
"heading_was_present": true,
"source_marker": {
"first_5_words": "We are looking for a",
"last_5_words": "deliveries into production."
},
"text": "We are looking for a Software Engineer versed in AI to lead the integration and deployment of AI solutions at an enterprise level. You will be responsible for designing and implementing AI-driven systems with a strong focus on scalability, security, and cost efficiency. You will also identify AI opportunities in our tech products \u0026 engineering processes and present these use case for prioritization.\n\nThis role requires deep expertise in \u0026 hands-on experience with LLMs, AI architecture, security, and responsible AI practices to ensure robust and compliant AI deployments \u2013 along with a proven track record of deliveries into production.",
"word_count": 102
},
{
"bullet_count": 7,
"heading": "Your expertise",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 hands-on experience in implementing",
"last_5_words": "and Agile development methodologies."
},
"text": "\u2022 hands-on experience in implementing real-world AI use cases.\n\u2022 expertise in Python, R or any related language, including AI frameworks like as LangChain and Semantic Kernel.\n\u2022 an AI-first mentality to problem solving and ideation.\n\u2022 strong experience in AI/ML integration with LLMs and Generative AI, RAG methodologies.\n\u2022 understanding of data privacy considerations and ethical practices in AI development.\n\u2022 prompt engineering expertise, including advanced prompting techniques.\n\u2022 knowledge of version control systems (e.g. Git) and Agile development methodologies.",
"word_count": 83
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "8826841a-c1ac-42d9-908e-a4f167d414ca",
"stage3_signals": {
"alias_found": true,
"alias_match_roles": [
{
"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
}
],
"kra_match_roles": [
{
"display_name": "AI Compliance Officer",
"kra_matches": [
{
"kra_text": "Assesses personal data usage, retention schedules, consent mechanisms, and cross-border transfer requirements for AI systems handling sensitive information.",
"sentence": "understanding of data privacy considerations and ethical practices in AI development.",
"similarity": 0.6538
},
{
"kra_text": "Reviews AI use cases and model deployments against applicable regulations, internal ethics policies, and governance guidelines prior to production approval.",
"sentence": "This role requires deep expertise in \u0026 hands-on experience with LLMs, AI architecture, security, and responsible AI practices to ensure robust and compliant AI deployments \u2013 along with a proven track record of deliveries into production.",
"similarity": 0.5625
},
{
"kra_text": "Monitors deployed AI systems for compliance policy drift, regulatory changes, and emerging requirements affecting existing AI deployments.",
"sentence": "You will be responsible for designing and implementing AI-driven systems with a strong focus on scalability, security, and cost efficiency.",
"similarity": 0.5041
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 12,
"score": 0.5735,
"slug": "ai-compliance-officer",
"total_count": null
},
{
"display_name": "AI Engineer",
"kra_matches": [
{
"kra_text": "Designs and implements prompt engineering workflows, few-shot examples, chain-of-thought patterns, and structured output parsing for AI feature pipelines.",
"sentence": "an AI-first mentality to problem solving and ideation.",
"similarity": 0.5352
},
{
"kra_text": "Designs and implements prompt engineering workflows, few-shot examples, chain-of-thought patterns, and structured output parsing for AI feature pipelines.",
"sentence": "prompt engineering expertise, including advanced prompting techniques.",
"similarity": 0.506
},
{
"kra_text": "Designs and implements prompt engineering workflows, few-shot examples, chain-of-thought patterns, and structured output parsing for AI feature pipelines.",
"sentence": "hands-on experience in implementing real-world AI use cases.",
"similarity": 0.5044
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 13,
"score": 0.5152,
"slug": "ai-engineer",
"total_count": null
},
{
"display_name": "ML Engineer",
"kra_matches": [
{
"kra_text": "Designs end-to-end ML training pipelines and model inference workflows using TensorFlow, PyTorch, or scikit-learn on cloud ML platforms.",
"sentence": "You will be responsible for designing and implementing AI-driven systems with a strong focus on scalability, security, and cost efficiency.",
"similarity": 0.4544
},
{
"kra_text": "Designs end-to-end ML training pipelines and model inference workflows using TensorFlow, PyTorch, or scikit-learn on cloud ML platforms.",
"sentence": "strong experience in AI/ML integration with LLMs and Generative AI, RAG methodologies.",
"similarity": 0.4091
},
{
"kra_text": "Designs end-to-end ML training pipelines and model inference workflows using TensorFlow, PyTorch, or scikit-learn on cloud ML platforms.",
"sentence": "This role requires deep expertise in \u0026 hands-on experience with LLMs, AI architecture, security, and responsible AI practices to ensure robust and compliant AI deployments \u2013 along with a proven track record of deliveries into production.",
"similarity": 0.4072
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 3,
"score": 0.4236,
"slug": "ml-engineer",
"total_count": null
},
{
"display_name": "MLOps Engineer",
"kra_matches": [
{
"kra_text": "Manages the end-to-end ML model release lifecycle from training job completion through validation gates to production deployment approval.",
"sentence": "This role requires deep expertise in \u0026 hands-on experience with LLMs, AI architecture, security, and responsible AI practices to ensure robust and compliant AI deployments \u2013 along with a proven track record of deliveries into production.",
"similarity": 0.4248
},
{
"kra_text": "Maintains model versioning, experiment lineage, and artifact tracking using MLflow, DVC, or Weights \u0026 Biases for reproducibility and auditability.",
"sentence": "strong experience in AI/ML integration with LLMs and Generative AI, RAG methodologies.",
"similarity": 0.4132
},
{
"kra_text": "Automates ML platform operations including scheduled retraining triggers, pipeline orchestration, evaluation workflows, and alerting configuration.",
"sentence": "You will be responsible for designing and implementing AI-driven systems with a strong focus on scalability, security, and cost efficiency.",
"similarity": 0.4063
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 16,
"score": 0.4148,
"slug": "ml-ops-engineer",
"total_count": null
},
{
"display_name": "Cyber Security Engineer",
"kra_matches": [
{
"kra_text": "Designs and implements security controls including SIEM integration, endpoint detection and response, identity management, and firewall rule management.",
"sentence": "You will be responsible for designing and implementing AI-driven systems with a strong focus on scalability, security, and cost efficiency.",
"similarity": 0.4423
},
{
"kra_text": "Performs threat modeling, security architecture reviews, and quantitative risk analysis for new product features and infrastructure changes.",
"sentence": "You will also identify AI opportunities in our tech products \u0026 engineering processes and present these use case for prioritization.",
"similarity": 0.406
},
{
"kra_text": "Designs and implements security controls including SIEM integration, endpoint detection and response, identity management, and firewall rule management.",
"sentence": "We are looking for a Software Engineer versed in AI to lead the integration and deployment of AI solutions at an enterprise level.",
"similarity": 0.372
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 5,
"score": 0.4067,
"slug": "cybersecurity-engineer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "MLOps Engineer",
"kra_matches": null,
"matched_count": 4,
"matched_skills": [
"LangChain",
"Python",
"R",
"Semantic Kernel"
],
"role_id": 16,
"score": 0.4,
"slug": "ml-ops-engineer",
"total_count": 10
},
{
"display_name": "ML Engineer",
"kra_matches": null,
"matched_count": 4,
"matched_skills": [
"LangChain",
"Python",
"R",
"Semantic Kernel"
],
"role_id": 3,
"score": 0.4,
"slug": "ml-engineer",
"total_count": 10
},
{
"display_name": "AI Engineer",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"LangChain",
"Semantic Kernel"
],
"role_id": 13,
"score": 0.2,
"slug": "ai-engineer",
"total_count": 10
},
{
"display_name": "Engineering Manager",
"kra_matches": null,
"matched_count": 2,
"matched_skills": [
"Agile",
"Python"
],
"role_id": 121,
"score": 0.2,
"slug": "engineering-manager",
"total_count": 10
},
{
"display_name": "AR/VR Engineer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"Python"
],
"role_id": 8,
"score": 0.1,
"slug": "ar-vr-engineer",
"total_count": 10
}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "DOMAIN",
"chosen_role": {
"display_name": "LLM / GenAI Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 151,
"score": 0.94,
"slug": "llm-genai-engineer",
"total_count": null
},
"confidence": 0.94,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [
"Enterprise AI solution integration and deployment",
"Scalable and cost-efficient AI system design",
"AI architecture",
"LLM and GenAI application development",
"Responsible and compliant AI delivery",
"AI opportunity identification and prioritization"
],
"matched_kras": [
"lead the integration and deployment of AI solutions",
"designing and implementing AI-driven systems",
"focus on scalability, security, and cost efficiency",
"identify AI opportunities in our tech products",
"present these use case for prioritization",
"hands-on experience in implementing real-world AI use cases",
"ensure robust and compliant AI deployments",
"proven track record of deliveries into production"
],
"matched_skills": [
"AI",
"Python",
"R",
"LangChain",
"Semantic Kernel",
"LLMs",
"Generative AI",
"RAG methodologies",
"prompt engineering",
"Git",
"Agile development methodologies",
"security",
"responsible AI practices",
"data privacy"
],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Domain=AI / ML; The JD centers on enterprise integration and deployment of LLM/GenAI solutions with RAG, prompt engineering, and AI application implementation, which best matches an LLM/GenAI Engineer.",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 15,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": {
"best_kra_similarity": 0.0,
"queue_id": 1877,
"r_and_r_preview": "We are looking for a Software Engineer versed in AI to lead the integration and deployment of AI solutions at an enterprise level. You will be responsible for designing and implementing AI-driven syst",
"role_display_name": "LLM / GenAI Engineer",
"role_slug": "llm-genai-engineer",
"status": "pending"
},
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 24450,
"role_display_name": "LLM / GenAI Engineer",
"role_slug": "llm-genai-engineer",
"skill_name": "Generative AI",
"status": "pending"
}
],
"queue_entry_id": null,
"v3_pipeline_triggered": false,
"v3_role_slug": null,
"v3_run_id": null
}
}
API 2 — extract-details
{
"alias_matches": [
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 67,
"existing_alias_text": "Python",
"input_term": "Python",
"matched_canonical": {
"category_id": 6,
"display_name": "Python",
"id": 5,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "python",
"sub_category_id": 96,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 430,
"existing_alias_text": "R",
"input_term": "R",
"matched_canonical": {
"category_id": 6,
"display_name": "R",
"id": 194,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "r",
"sub_category_id": 96,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 501,
"existing_alias_text": "LangChain",
"input_term": "LangChain",
"matched_canonical": {
"category_id": 5,
"display_name": "LangChain",
"id": 240,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "langchain",
"sub_category_id": 146,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 1971,
"existing_alias_text": "Semantic Kernel",
"input_term": "Semantic Kernel",
"matched_canonical": {
"category_id": 5,
"display_name": "Semantic Kernel",
"id": 1335,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "semantic-kernel",
"sub_category_id": 146,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 1829,
"existing_alias_text": "LLMs",
"input_term": "LLMs",
"matched_canonical": {
"category_id": 2,
"display_name": "LLMs",
"id": 1193,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "llms",
"sub_category_id": 903,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 1830,
"existing_alias_text": "RAG",
"input_term": "RAG",
"matched_canonical": {
"category_id": 2,
"display_name": "RAG",
"id": 1194,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "rag",
"sub_category_id": 904,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 1843,
"existing_alias_text": "Prompt engineering",
"input_term": "Prompt Engineering",
"matched_canonical": {
"category_id": 8,
"display_name": "Prompt engineering",
"id": 1207,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "prompt-engineering",
"sub_category_id": 914,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 1613,
"existing_alias_text": "Git",
"input_term": "Git",
"matched_canonical": {
"category_id": 13,
"display_name": "Git",
"id": 1002,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "git",
"sub_category_id": 730,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
},
{
"alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
"alias_persisted": false,
"existing_alias_id": 868,
"existing_alias_text": "Agile",
"input_term": "Agile",
"matched_canonical": {
"category_id": 8,
"display_name": "Agile",
"id": 520,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "agile",
"sub_category_id": 3594,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"matched_via": "alias"
}
],
"candidate_roles": [
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
},
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 435,
"rationale": null,
"role_archetype": "Engineering",
"slug": "fullstack-developer",
"source": "db"
},
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
},
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
},
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
},
{
"display_name": "AR/VR Engineer",
"id": 8,
"rationale": null,
"role_archetype": null,
"slug": "ar-vr-engineer",
"source": "db"
},
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
},
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
}
],
"chosen_role": {
"display_name": "LLM / GenAI Engineer",
"id": 151,
"rationale": "Domain=AI / ML; The JD centers on enterprise integration and deployment of LLM/GenAI solutions with RAG, prompt engineering, and AI application implementation, which best matches an LLM/GenAI Engineer.",
"role_archetype": null,
"slug": "llm-genai-engineer",
"source": "db"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Scripting \u0026 DSL Languages",
"id": 248,
"rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
"slug": "cloud-security-scripting-dsl-languages",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages",
"id": 1,
"rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
"slug": "programming-languages",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Backend Developer",
"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"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages \u0026 DSLs",
"id": 475,
"rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
"slug": "programming-languages-dsls",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages and Scripting",
"id": 59,
"rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
"slug": "programming-languages-and-scripting",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 21,
"rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for ML Systems",
"id": 39,
"rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
"slug": "programming-languages-for-ml-systems",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "ML Engineer",
"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"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for XR",
"id": 97,
"rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
"slug": "programming-languages-for-xr",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AR/VR Engineer",
"id": 8,
"rationale": null,
"role_archetype": null,
"slug": "ar-vr-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Python Programming",
"id": 290,
"rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
"slug": "python-programming",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for ML Systems",
"id": 39,
"rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
"slug": "programming-languages-for-ml-systems",
"source": "db"
},
"input_skill": "R",
"llm_role": null,
"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"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "LLM Operations and Orchestration",
"id": 49,
"rationale": "Operational stack for building, serving, evaluating, and orchestrating LLM-based systems. This includes vector retrieval, prompt workflows, LLM serving, and observability for generative applications.",
"slug": "llm-operations-and-orchestration",
"source": "db"
},
"input_skill": "LangChain",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "LLM Operations and Orchestration",
"id": 49,
"rationale": "Operational stack for building, serving, evaluating, and orchestrating LLM-based systems. This includes vector retrieval, prompt workflows, LLM serving, and observability for generative applications.",
"slug": "llm-operations-and-orchestration",
"source": "db"
},
"input_skill": "Semantic Kernel",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
]
},
{
"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": "LLMs",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "RAG",
"llm_role": null,
"roles_from_db": []
},
{
"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": "Prompt Engineering",
"llm_role": null,
"roles_from_db": []
},
{
"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": "Git",
"llm_role": null,
"roles_from_db": []
},
{
"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": "Agile",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Software Concepts, Patterns \u0026 Practices",
"id": 478,
"rationale": "Champion foundational software design patterns, development methodologies, and engineering best practices.",
"slug": "software-concepts-patterns-practices",
"source": "db"
},
"input_skill": "Agile",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
]
}
],
"input_final_skills": [
"Python",
"R",
"LangChain",
"Semantic Kernel",
"LLMs",
"Generative AI",
"RAG",
"Prompt Engineering",
"Git",
"Agile"
],
"input_llm_skills": [
"Python",
"R",
"LangChain",
"Semantic Kernel",
"LLMs",
"Generative AI",
"RAG",
"Prompt Engineering",
"Git",
"Agile"
],
"new_aliases_persisted": 0,
"run_id": "8826841a-c1ac-42d9-908e-a4f167d414ca",
"skills_detail": [
{
"aliases_in_db": [
{
"alias_text": "Python",
"alias_type": "CANONICAL",
"id": 67,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Python 2",
"alias_type": "VERSION",
"id": 72,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Python 2.x",
"alias_type": "VERSION",
"id": 74,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Python 3",
"alias_type": "VERSION",
"id": 73,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Python 3.10",
"alias_type": "VERSION",
"id": 76,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Python 3.11",
"alias_type": "VERSION",
"id": 77,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Python 3.12",
"alias_type": "VERSION",
"id": 78,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "Python 3.x",
"alias_type": "VERSION",
"id": 75,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "py",
"alias_type": "VERSION",
"id": 2183,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "py2",
"alias_type": "VERSION",
"id": 68,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "py3",
"alias_type": "VERSION",
"id": 69,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "python 3",
"alias_type": "VERSION",
"id": 2186,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "python 3.x",
"alias_type": "VERSION",
"id": 2849,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "python2",
"alias_type": "VERSION",
"id": 70,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "python3",
"alias_type": "VERSION",
"id": 71,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "python3.x",
"alias_type": "VERSION",
"id": 2848,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 6,
"display_name": "Python",
"id": 5,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "python",
"sub_category_id": 96,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Cloud Security Scripting \u0026 DSL Languages",
"id": 248,
"rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
"slug": "cloud-security-scripting-dsl-languages",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cloud Security Engineer",
"id": 23,
"rationale": null,
"role_archetype": null,
"slug": "cloud-security-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages",
"id": 1,
"rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
"slug": "programming-languages",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Backend Developer",
"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"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages \u0026 DSLs",
"id": 475,
"rationale": "Oversee and guide the selection and effective use of programming and domain\u2010specific languages in software projects.",
"slug": "programming-languages-dsls",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages and Scripting",
"id": 59,
"rationale": "Languages used to write security automation, analysis scripts, detection logic, and remediation helpers. This is the primary implementation surface for a cybersecurity engineer across tooling and response workflows.",
"slug": "programming-languages-and-scripting",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Cyber Security Engineer",
"id": 5,
"rationale": null,
"role_archetype": null,
"slug": "cybersecurity-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for Data Work",
"id": 21,
"rationale": "Languages used to implement data pipelines, transformations, and operational glue. This is the primary coding surface for building ingestion, enrichment, and automation logic in data engineering.",
"slug": "programming-languages-for-data-work",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Data Engineer",
"id": 2,
"rationale": null,
"role_archetype": null,
"slug": "data-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for ML Systems",
"id": 39,
"rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
"slug": "programming-languages-for-ml-systems",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "ML Engineer",
"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"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for XR",
"id": 97,
"rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
"slug": "programming-languages-for-xr",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AR/VR Engineer",
"id": 8,
"rationale": null,
"role_archetype": null,
"slug": "ar-vr-engineer",
"source": "db"
}
]
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Python Programming",
"id": 290,
"rationale": "Core Python language skills used to implement backend business logic, request handlers, integrations, and service internals. This is the primary coding surface for the role.",
"slug": "python-programming",
"source": "db"
},
"input_skill": "Python",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Python Backend Developer",
"id": 80,
"rationale": null,
"role_archetype": "Engineering",
"slug": "python-backend-developer",
"source": "db"
}
]
}
],
"input_skill": "Python",
"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": "R",
"alias_type": "VERSION",
"id": 430,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "R 3",
"alias_type": "VERSION",
"id": 432,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "R 3.x",
"alias_type": "VERSION",
"id": 434,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "R 4",
"alias_type": "VERSION",
"id": 433,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "R 4.0",
"alias_type": "VERSION",
"id": 435,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "R 4.1",
"alias_type": "VERSION",
"id": 436,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "R 4.2",
"alias_type": "VERSION",
"id": 437,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "R 4.3",
"alias_type": "VERSION",
"id": 438,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "R 4.4",
"alias_type": "VERSION",
"id": 439,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
},
{
"alias_text": "R 4.x",
"alias_type": "VERSION",
"id": 440,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 6,
"display_name": "R",
"id": 194,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "r",
"sub_category_id": 96,
"typical_lifespan": "EVERGREEN",
"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages for ML Systems",
"id": 39,
"rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
"slug": "programming-languages-for-ml-systems",
"source": "db"
},
"input_skill": "R",
"llm_role": null,
"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"
}
]
}
],
"input_skill": "R",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "LangChain",
"alias_type": "CANONICAL",
"id": 501,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 5,
"display_name": "LangChain",
"id": 240,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "langchain",
"sub_category_id": 146,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "LLM Operations and Orchestration",
"id": 49,
"rationale": "Operational stack for building, serving, evaluating, and orchestrating LLM-based systems. This includes vector retrieval, prompt workflows, LLM serving, and observability for generative applications.",
"slug": "llm-operations-and-orchestration",
"source": "db"
},
"input_skill": "LangChain",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
]
}
],
"input_skill": "LangChain",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Semantic Kernel",
"alias_type": "CANONICAL",
"id": 1971,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 5,
"display_name": "Semantic Kernel",
"id": 1335,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "semantic-kernel",
"sub_category_id": 146,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "LLM Operations and Orchestration",
"id": 49,
"rationale": "Operational stack for building, serving, evaluating, and orchestrating LLM-based systems. This includes vector retrieval, prompt workflows, LLM serving, and observability for generative applications.",
"slug": "llm-operations-and-orchestration",
"source": "db"
},
"input_skill": "Semantic Kernel",
"llm_role": null,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
]
}
],
"input_skill": "Semantic Kernel",
"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": "LLMs",
"alias_type": "CANONICAL",
"id": 1829,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 2,
"display_name": "LLMs",
"id": 1193,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "llms",
"sub_category_id": 903,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "LLMs",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "LLMs",
"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": "Generative AI",
"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": "generative-ai",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "RAG",
"alias_type": "CANONICAL",
"id": 1830,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 2,
"display_name": "RAG",
"id": 1194,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "CONCEPT",
"slug": "rag",
"sub_category_id": 904,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "RAG",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "RAG",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Prompt engineering",
"alias_type": "CANONICAL",
"id": 1843,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 8,
"display_name": "Prompt engineering",
"id": 1207,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "prompt-engineering",
"sub_category_id": 914,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"input_skill": "Prompt Engineering",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Prompt Engineering",
"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": "Git",
"alias_type": "CANONICAL",
"id": 1613,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 13,
"display_name": "Git",
"id": 1002,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "TOOL",
"slug": "git",
"sub_category_id": 730,
"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": "Git",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Git",
"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": "Agile",
"alias_type": "CANONICAL",
"id": 868,
"is_primary": true,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 8,
"display_name": "Agile",
"id": 520,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "METHODOLOGY",
"slug": "agile",
"sub_category_id": 3594,
"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": "Agile",
"llm_role": null,
"roles_from_db": []
},
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Software Concepts, Patterns \u0026 Practices",
"id": 478,
"rationale": "Champion foundational software design patterns, development methodologies, and engineering best practices.",
"slug": "software-concepts-patterns-practices",
"source": "db"
},
"input_skill": "Agile",
"llm_role": null,
"roles_from_db": [
{
"display_name": "Engineering Manager",
"id": 121,
"rationale": null,
"role_archetype": null,
"slug": "engineering-manager",
"source": "db"
}
]
}
],
"input_skill": "Agile",
"matched_via": "alias",
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"Generative AI"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "LLM / GenAI Engineer",
"id": 151,
"rationale": "Domain=AI / ML; The JD centers on enterprise integration and deployment of LLM/GenAI solutions with RAG, prompt engineering, and AI application implementation, which best matches an LLM/GenAI Engineer.",
"role_archetype": null,
"slug": "llm-genai-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Python",
"tag": "in_db"
},
{
"skill": "R",
"tag": "in_db"
},
{
"skill": "LangChain",
"tag": "in_db"
},
{
"skill": "Semantic Kernel",
"tag": "in_db"
},
{
"skill": "LLMs",
"tag": "in_db"
},
{
"skill": "Generative AI",
"tag": "new"
},
{
"skill": "RAG",
"tag": "in_db"
},
{
"skill": "Prompt Engineering",
"tag": "in_db"
},
{
"skill": "Git",
"tag": "in_db"
},
{
"skill": "Agile",
"tag": "in_db"
}
],
"llm_cost_api1_usd": null,
"llm_cost_api2_usd": null,
"llm_cost_api3_usd": null,
"llm_cost_total_usd": null,
"persistence": {
"items": [
{
"chosen_role_id": 151,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "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": 151,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Programming Languages",
"id": 1,
"rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
"slug": "programming-languages",
"source": "db"
},
"dimension_id": 1,
"input_skill": "Python",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "Backend Developer",
"id": 1,
"rationale": null,
"role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
"slug": "backend-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 15,
"rationale": null,
"role_archetype": null,
"slug": "full-stack-engineer",
"source": "db"
},
{
"display_name": "Fullstack Developer",
"id": 435,
"rationale": null,
"role_archetype": "Engineering",
"slug": "fullstack-developer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 5,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 151,
"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": 151,
"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": 151,
"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": 151,
"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": 151,
"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": 151,
"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": 151,
"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": "R",
"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": 194,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 151,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "LLM Operations and Orchestration",
"id": 49,
"rationale": "Operational stack for building, serving, evaluating, and orchestrating LLM-based systems. This includes vector retrieval, prompt workflows, LLM serving, and observability for generative applications.",
"slug": "llm-operations-and-orchestration",
"source": "db"
},
"dimension_id": 49,
"input_skill": "LangChain",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
},
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
{
"display_name": "MLOps Engineer",
"id": 16,
"rationale": null,
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
}
],
"skill_dimension_saved": true,
"skill_id": 240,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 151,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "LLM Operations and Orchestration",
"id": 49,
"rationale": "Operational stack for building, serving, evaluating, and orchestrating LLM-based systems. This includes vector retrieval, prompt workflows, LLM serving, and observability for generative applications.",
"slug": "llm-operations-and-orchestration",
"source": "db"
},
"dimension_id": 49,
"input_skill": "Semantic Kernel",
"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": 1335,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 151,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 96,
"input_skill": "LLMs",
"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": 1193,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 151,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 96,
"input_skill": "RAG",
"llm_role": null,
"matched_chosen_role": false,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
"role_dimension_saved": false,
"roles_from_db": [],
"skill_dimension_saved": true,
"skill_id": 1194,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 151,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "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": "Prompt Engineering",
"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": 1207,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 151,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 96,
"input_skill": "Git",
"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": 1002,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 151,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "React Frontend Development",
"id": 96,
"rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
"slug": "d_init_01",
"source": "db"
},
"dimension_id": 96,
"input_skill": "Agile",
"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": 520,
"skill_tag": "in_db",
"skipped_reason": null
},
{
"chosen_role_id": 151,
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Software Concepts, Patterns \u0026 Practices",
"id": 478,
"rationale": "Champion foundational software design patterns, development methodologies, and engineering best practices.",
"slug": "software-concepts-patterns-practices",
"source": "db"
},
"dimension_id": 478,
"input_skill": "Agile",
"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": 520,
"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": "8826841a-c1ac-42d9-908e-a4f167d414ca"
}