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Pipeline run

8826841a-c1ac-42d9-908e-a4f167d414ca

Pipeline LLM cost (USD)
API 1: $0.0081 API 2: $0.0001 API 3: $0.0000 Total: $0.0082

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work · AI/ML Engineering
Lead enterprise AI solution integration and deployment: design scalable, secure, cost-efficient AI systems, identify and prioritize AI use cases, and build production LLM/RAG apps with responsible AI practices.
"lead the integration and deployment of AI solutions at an enterprise level"
Tech stack maturity
Modern Cloud Native
The skill set centers on contemporary GenAI tooling and practices such as LangChain, RAG, prompt engineering, semantic kernel, and Python, which aligns with a modern cloud-native AI stack.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
3.20 / 5
Title match
Has AI skill
AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2): LangChain, Semantic Kernel
Models / concepts (×3): RAG, LLMs, prompt engineering, AI, ML, AI/ML, Generative AI, Artificial Intelligence
Evidence — skills matched in JD (10)
Python R LangChain Semantic Kernel LLMs Generative AI RAG Prompt Engineering Git Agile
Skill cluster (2 dimension groups, role-scoped)
Python Programming
Python
Cross-cutting / unaligned
R LangChain Semantic Kernel LLMs Generative AI RAG Prompt Engineering Git Agile
Show KRA description ↓
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. • 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.

Signals

Skill ml-ops-engineer
0.40
Alias ai-engineer
1.00
KRA ai-compliance-officer
0.57

Post-classification

Centroidupdated · n=15
Alias collision log
New-role queue
New skills captured1
New KRA capturedyes

Captured for admin review

Generative AI primary LLM / GenAI Engineer pending
R&R fragment (sim 0.00) LLM / GenAI Engineer pending

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…

Status: completed Created: 2026-05-27T17:37:01.740850Z Updated: 2026-05-27T17:37:44.360529Z API 3 duration: 9452 ms
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

LLM / GenAI Engineer

domain · AI / ML CASE DOMAIN

slug: 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

AIPythonRLangChainSemantic KernelLLMsGenerative AIRAG methodologiesprompt engineeringGitAgile development methodologiessecurityresponsible AI practicesdata privacy

Matched dimensions

Enterprise AI solution integration and deploymentScalable and cost-efficient AI system designAI architectureLLM and GenAI application developmentResponsible and compliant AI deliveryAI opportunity identification and prioritization

Matched KRAs

lead the integration and deployment of AI solutionsdesigning and implementing AI-driven systemsfocus on scalability, security, and cost efficiencyidentify AI opportunities in our tech productspresent these use case for prioritizationhands-on experience in implementing real-world AI use casesensure robust and compliant AI deploymentsproven track record of deliveries into production

Resolution: in_db — role exists in library; skill↔dim and role↔dim links saved when applicable.

0
New skills
0
Skill↔dim saved
0
Role↔dim saved
0
Skipped

Job description

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.

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

Aliases — catalog

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

Context tags (catalog)

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

Stored enrichment (catalog DB)

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

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

Skill profile (library / DB)

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

Dimensions (API 2 worklist)

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

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

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

  • Programming Languages & DSLs Catalog dimension db id 475

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

  • Programming Languages and Scripting Catalog dimension db id 59

    Library dimension (catalog)

    Roles linked in library: Cyber Security Engineer

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

  • Programming Languages for XR Catalog dimension db id 97

    Library dimension (catalog)

    Roles linked in library: AR/VR Engineer

  • Python Programming Catalog dimension db id 290

    Library dimension (catalog)

    Roles linked in library: Python Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
R Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: R id=194 · r

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)

Bioconductor CRAN R Markdown Shiny Tidyverse caret data.table dplyr ggplot2 glm lme4 lubridate rstan tidyr tidyverse

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)
LangChain Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: LangChain id=240 · langchain

Aliases — catalog

  • LangChain (CANONICAL) primary

Context tags (catalog)

API integration Hugging Face LLM LLMs OpenAI RAG agents callbacks chains data augmentation deployment document loaders embeddings fine-tuning memory prompt engineering prompt templates prompts retrieval retrievers state management streaming text splitters toolkits tools vector database vector stores

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)
Semantic Kernel Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Semantic Kernel id=1335 · semantic-kernel

Aliases — catalog

  • Semantic Kernel (CANONICAL) primary

Context tags (catalog)

AI orchestration API integration LLM cognitive services contextual understanding data augmentation knowledge graph machine learning model integration multi-modal natural language processing plugin prompt engineering semantic search workflow automation

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)
LLMs Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: LLMs id=1193 · llms

Aliases — catalog

  • LLMs (CANONICAL)

Context tags (catalog)

BERT GPT NLP attention mechanism contextual embeddings fine-tuning language generation model training pre-trained models prompt engineering text classification tokenization transfer learning transformers zero-shot learning

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)
Generative AI Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
RAG Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: RAG id=1194 · rag

Aliases — catalog

  • RAG (CANONICAL)

Context tags (catalog)

AI applications contextualization data augmentation fine-tuning generation information retrieval knowledge integration machine learning model training natural language processing prompt engineering retrieval semantic search transformer models user intent

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)
Prompt Engineering Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Prompt engineering id=1207 · prompt-engineering

Aliases — catalog

  • Prompt engineering (CANONICAL)

Context tags (catalog)

AI alignment contextual prompts data annotation evaluation metrics feedback loops fine-tuning iterative testing language models model training natural language processing prompt design prompt optimization prompt templates user experience user intent

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)
Git Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Git id=1002 · git

Aliases — catalog

  • Git (CANONICAL)

Context tags (catalog)

CI/CD GitHub GitLab branching checkout clone commit fork merging pull request rebase remote repository stash versioning

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)
Agile Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Agile id=520 · agile

Aliases — catalog

  • Agile (CANONICAL) primary

Context tags (catalog)

Kanban SAFe Scrum backlog backlog grooming burndown burndown chart continuous delivery continuous improvement cross-functional daily standup epics incremental development iteration iteration planning lean product backlog product owner retrospective sprint sprint planning stand-up story points user stories velocity

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
RoleSoftware Engineer
CompanyUBS
DomainFinancial Services
Location Pune, India (hybrid)
JD type pass
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
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  "nano_parsed": {
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    },
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          "Pune, MH"
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          {
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    "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.",
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}
API 2 — extract-details
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      "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",
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