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Pipeline run

afe8eb6e-990a-4496-8b36-09eea3b1a6d1

Pipeline LLM cost (USD)
API 1: $0.0028 API 2: $0.0558 API 3: $0.0000 Total: $0.0586

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work
no_db_connection
Tech stack maturity
Mainstream Modern
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
1.70 / 5
· Title match
Has AI skill
AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3): LLM, LLMs, AI
Evidence — skills matched in JD (6)
Large Language Models Cloud Native AI Security Reliability Efficiency
Skill cluster (0 dimension groups, role-scoped)
No dimension groups computed for this JD.
Show KRA description ↓
Expected to perform independently and become an SME. Required active participation/contribution in team discussions. Contribute in providing solutions to work related problems. Collaborate with cross-functional teams to optimize deployment workflows and operational processes. Assist in documenting operational procedures and best practices to support knowledge sharing. Support junior team members by providing guidance and sharing expertise to foster professional growth. Must To Have Skills: Proficiency in Large Language Models (LLMs). Experience with cloud-native services and tools for deployment and monitoring of AI models. Strong understanding of operational challenges related to AI systems and strategies to address them. Familiarity with compliance and security standards applicable to AI and data operations. Ability to analyze system performance metrics and implement improvements for reliability and efficiency.
Status: completed Created: 2026-05-13T12:55:48.819567Z Updated: 2026-05-13T12:57:44.382870Z API 3 duration: 4366 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 Operations Engineer

slug: llm-operations-engineer · id: — · source: llm

The primary skills focus on AI and cloud technologies, which align with the LLM operations role.

Resolution: human_review_required — role not in DB; role↔dimension links may be deferred.

5
New skills
6
Skill↔dim saved
0
Role↔dim saved
0
Skipped

Job description

About the job
Project Role : LLM Operations Engineer

Project Role Description : Utilize cloud-native services and tools for scalable and efficient deployment. Monitor LLM performance, address operational challenges, and ensure compliance and security standards in AI operations.

Must have skills : Large Language Models (LLMs)

Good to have skills : NA

Minimum 3 Year(s) Of Experience Is Required

Educational Qualification : 15 years full time education

Summary:

As a Large Language Model Operations Engineer, a typical day involves leveraging cloud-native platforms and tools to deploy language models in a scalable and efficient manner. The role includes continuous monitoring of model performance to ensure optimal functioning, proactively identifying and resolving operational issues, and maintaining adherence to compliance and security protocols within AI operations. This position requires a dynamic approach to managing complex systems and collaborating with various teams to support seamless AI service delivery.

Roles & Responsibilities:

 Expected to perform independently and become an SME.
 Required active participation/contribution in team discussions.
 Contribute in providing solutions to work related problems.
 Collaborate with cross-functional teams to optimize deployment workflows and operational processes.
 Assist in documenting operational procedures and best practices to support knowledge sharing.
 Support junior team members by providing guidance and sharing expertise to foster professional growth.


Professional & Technical Skills:

 Must To Have Skills: Proficiency in Large Language Models (LLMs).
 Experience with cloud-native services and tools for deployment and monitoring of AI models.
 Strong understanding of operational challenges related to AI systems and strategies to address them.
 Familiarity with compliance and security standards applicable to AI and data operations.
 Ability to analyze system performance metrics and implement improvements for reliability and efficiency.


Additional Information:

 The candidate should have minimum 3 years of experience in Large Language Models (LLMs).
 This position is based at our Gurugram office.
 A 15 years full time education is required.


, 15 years full time education

Skills from this JD

Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.

Large Language Models Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.93

LLMs are now a hiring-pipeline staple: job postings commonly ask for prompt engineering, RAG, and OpenAI/Anthropic integration, and major cloud vendors ship managed LLM services.

Vendor & license

(0.99)

Context keywords
transformer attention tokenization prompt engineering fine-tuning inference embedding context window RAG instruction tuning RLHF few-shot zero-shot hallucination vector database
Ambiguity low

The term is specific and commonly used in JDs to mean LLMs. It is unlikely to be confused with a different catalog skill in typical hiring context.

Versioning

Not versioned

Type assignment

Concept ·machine_learning_model_concept confidence 0.94

Large Language Models are a named knowledge unit about a class of machine learning models, so by the Concept vs Methodology rule they are a Concept rather than a tool or framework.

Derived legacy fields
Category
Concept
Sub-category
machine_learning_model_concept
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
NOT_APPLICABLE

Dimensions (API 2 worklist)

  • Version Control Systems Catalog dimension db id 365

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • Large Language Models

    Pipeline tentative id

    Covers the core concepts, capabilities, and operational concerns of LLMs as model artifacts and inference systems. This skill belongs here because it refers to the model class itself, including how LLMs are selected, evaluated, and used in production AI workflows.

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Version Control Systems
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Native Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.93

Cloud-native is broadly adopted: it appears in many job descriptions and is a standard focus in CNCF/Kubernetes ecosystems, with major vendors marketing cloud-native platforms and tooling.

Vendor & license

(0.98)

Context keywords
Kubernetes containers microservices service mesh Docker Helm Istio CI/CD DevOps observability autoscaling immutable infrastructure 12-factor app serverless GitOps
Ambiguity low

“Cloud Native” is a fairly specific architecture term in JDs and is usually distinguishable from generic cloud skills or vendor products; typical extractors are unlikely to confuse it with another catalog skill.

Versioning

Not versioned

Type assignment

Architecture ·cloud_native_architecture confidence 0.90

By the Architecture vs Concept rule, Cloud Native is a system-shape pattern for building and operating software in cloud environments, not a knowledge unit or methodology.

Derived legacy fields
Category
Architecture
Sub-category
cloud_native_architecture
Skill nature
PATTERN
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
NOT_APPLICABLE

Dimensions (API 2 worklist)

  • Version Control Systems Catalog dimension db id 365

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • Cloud Native Architecture

    Pipeline tentative id

    Designing applications and services to run effectively in cloud-managed environments using elastic, distributed, and container-friendly patterns. Cloud Native fits here because it describes the architectural style and operational assumptions behind modern cloud-first systems.

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Version Control Systems
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
AI Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: AI id=2634 · ai

Aliases — catalog

  • NgRx (CANONICAL) primary
  • ngrx 17 (VERSION)
  • ngrx v17 (VERSION)
  • ngrx17 (VERSION)
  • ngrx@17 (VERSION)

Context tags (catalog)

Actions Angular DevTools Effects Entity State Immutable State NgRx Component Store NgRx Effects NgRx Router Store NgRx Schematics NgRx Store Redux RxJS Selectors State Management Store actions effects entity state immutable state observables reducers selectors state management store

Stored enrichment (catalog DB)

Category
Library
Sub-category
State Management Library
Vendor
NgRx Team
License
mit
Year introduced
2016
Confidence
0.96
Version strategy
SEPARATE_ENTITY
Version tag
17

Maturity reasoning: NgRx appears in many Angular job descriptions and is a common enterprise state-management choice; its GitHub ecosystem remains active, indicating broad adoption rather than niche use.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
2147
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Version Control Systems Catalog dimension db id 365

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Version Control Systems
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Security Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.96

Security is a standard requirement in most engineering JDs (AppSec, cloud security, IAM, SOC2), and major vendors like AWS/Azure/GCP all market security services as core platform capabilities.

Vendor & license

(0.99)

Context keywords
threat modeling vulnerability assessment penetration testing SIEM IDS/IPS zero trust IAM least privilege incident response SOC CVE CWE OWASP encryption risk assessment
Ambiguity low

“Security” is a broad domain term, but in JDs it usually denotes the cybersecurity/security function itself rather than a different catalog skill. It’s not a short acronym or overloaded product name.

Versioning

Not versioned

Type assignment

Domain ·security confidence 0.98

Security is a vertical/problem-space body of knowledge rather than a tool, framework, or methodology, so it fits the Domain type.

Derived legacy fields
Category
Domain
Sub-category
security
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
NOT_APPLICABLE

Dimensions (API 2 worklist)

  • Version Control Systems Catalog dimension db id 365

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • Security Fundamentals

    Pipeline tentative id

    Core security concepts and controls that apply across systems, services, and operations. This skill is broad and not specific enough to fit a narrower catalog dimension, so it belongs in a foundational security cluster.

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Version Control Systems
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Reliability Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.93

Reliability is a standard SRE/DevOps hiring requirement; job postings commonly ask for SLIs/SLOs, incident response, and high-availability design across cloud platforms.

Vendor & license

(0.99)

Context keywords
fault tolerance redundancy high availability failover resilience MTBF MTTR SLA incident response disaster recovery observability monitoring capacity planning service uptime error budget
Ambiguity low

“Reliability” is a broad concept but not a short acronym or overloaded product name; in JDs it usually clearly means system reliability/resilience, not a distinct catalog skill.

Versioning

Not versioned

Type assignment

Concept ·reliability confidence 0.96

Reliability is a named knowledge unit about system behavior and quality, so by the Concept vs Methodology rule it is a Concept rather than a way of working or an architecture.

Derived legacy fields
Category
Concept
Sub-category
reliability
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
NOT_APPLICABLE

Dimensions (API 2 worklist)

  • Version Control Systems Catalog dimension db id 365

    Library dimension (catalog)

Locked dimensions (v3 placement)

  • System Reliability Engineering

    Pipeline tentative id

    Practices for keeping systems available, resilient, and recoverable under failure or load. This fits the target skill because reliability is the umbrella concern for uptime, fault tolerance, incident readiness, and safe recovery.

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Version Control Systems
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Efficiency Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

Maturity well_known confidence 0.93

Efficiency is a core performance-tuning principle referenced across most engineering JDs and capacity-planning roles; it’s a standard expectation in system design, SRE, and backend interviews rather than a niche tool or sunset practice.

Vendor & license

(0.99)

Context keywords
throughput latency resource utilization bottleneck analysis profiling benchmarking capacity planning performance tuning scalability load testing optimization CPU utilization memory footprint response time
Ambiguity flagged

Could be confused with: efficiency_ratio, energy_efficiency

"Efficiency" is a broad concept and in JDs could refer to efficiency ratio or energy efficiency rather than the general performance-tuning principle, so an extractor may conflate it with related catalog skills.

Versioning

Not versioned

Type assignment

Concept ·efficiency_principle confidence 0.93

Efficiency is a named knowledge unit about minimizing resource use or waste, so by the Concept vs Methodology rule it is a Concept rather than a way of working.

Derived legacy fields
Category
Concept
Sub-category
efficiency_principle
Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
NOT_APPLICABLE

Dimensions (API 2 worklist)

  • Capacity Planning and Performance Tuning Catalog dimension db id 284

    Library dimension (catalog)

    Roles linked in library: Network Engineer, Virtualization Engineer

  • Version Control Systems Catalog dimension db id 365

    Library dimension (catalog)

  • Capacity Planning and Performance Tuning Catalog dimension db id 284

    Library dimension (catalog)

    Roles linked in library: Network Engineer, Virtualization Engineer

Locked dimensions (v3 placement)

  • Capacity Planning and Performance Tuning

    Reuses catalog slug

    Covers improving system throughput, latency, and resource utilization by sizing workloads, removing bottlenecks, and tuning configurations. Efficiency fits here when it means getting more useful work from the same compute, memory, storage, or network budget.

  • Algorithmic Efficiency

    Pipeline tentative id

    Covers choosing and analyzing approaches that reduce time or space complexity in code and data processing. Efficiency belongs here when it refers to making an implementation faster, smaller, or less computationally expensive.

  • Capacity Planning and Performance Tuning

    Reuses catalog slug

    Forecasting and tuning the virtualization estate to maintain headroom and acceptable performance. This includes analyzing contention, balancing density, and responding to growth trends.

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Capacity Planning and Performance Tuning
capacity-planning-and-performance-tuning
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Version Control Systems
d_init_01
New skill saved · 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
AI in_db
Version Control Systems
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Large Language Models in_db
Version Control Systems
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Native in_db
Version Control Systems
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Security in_db
Version Control Systems
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Reliability in_db
Version Control Systems
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Efficiency in_db
Capacity Planning and Performance Tuning
capacity-planning-and-performance-tuning
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Efficiency in_db
Version Control Systems
d_init_01
New skill saved · Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_added Large Language Models 2695
canonical_skill_added Cloud Native 2696
canonical_skill_added Security 2697
canonical_skill_added Reliability 2698
canonical_skill_added Efficiency 2699
dimension_skill_link Large Language Models ↔ Version Control Systems 365
dimension_skill_link Cloud Native ↔ Version Control Systems 365
dimension_skill_link Security ↔ Version Control Systems 365
dimension_skill_link Reliability ↔ Version Control Systems 365
dimension_skill_link Efficiency ↔ Capacity Planning and Performance Tuning 284
dimension_skill_link Efficiency ↔ Version Control Systems 365
nano JD Parser — gpt-4.1-nano click to toggle
RoleLLM Operations Engineer
ExperienceMinimum 3 Year(s) Of Experience Is Required
DomainOther
Location Gurugram, India (null)
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [],
  "company_name": null,
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [],
      "domain": "Other"
    },
    "secondary": null
  },
  "education": [
    {
      "level": "null",
      "qualification": "null - null",
      "raw": "15 years full time education",
      "requirement": "required"
    }
  ],
  "experience": {
    "max": null,
    "min": 3,
    "raw": "Minimum 3 Year(s) Of Experience Is Required"
  },
  "job_locations": [
    {
      "aliases": [
        "Gurgaon"
      ],
      "city": "Gurugram",
      "country": "India",
      "state": null,
      "work_mode": "null"
    }
  ],
  "role": "LLM Operations Engineer",
  "role_archetype": "Other",
  "roles_and_responsibilities": [
    {
      "bullet_count": 6,
      "heading": "Roles \u0026 Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Expected to perform independently and",
        "last_5_words": "guidance and sharing expertise to"
      },
      "text": "Expected to perform independently and become an SME.\nRequired active participation/contribution in team discussions.\nContribute in providing solutions to work related problems.\nCollaborate with cross-functional teams to optimize deployment workflows and operational processes.\nAssist in documenting operational procedures and best practices to support knowledge sharing.\nSupport junior team members by providing guidance and sharing expertise to foster professional growth.",
      "word_count": 56
    },
    {
      "bullet_count": 5,
      "heading": "Professional \u0026 Technical Skills",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "Must To Have Skills: Proficiency",
        "last_5_words": "for reliability and efficiency."
      },
      "text": "Must To Have Skills: Proficiency in Large Language Models (LLMs).\nExperience with cloud-native services and tools for deployment and monitoring of AI models.\nStrong understanding of operational challenges related to AI systems and strategies to address them.\nFamiliarity with compliance and security standards applicable to AI and data operations.\nAbility to analyze system performance metrics and implement improvements for reliability and efficiency.",
      "word_count": 66
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Large Language Models"
    },
    {
      "is_primary": true,
      "skill_name": "Cloud Native"
    },
    {
      "is_primary": true,
      "skill_name": "AI"
    },
    {
      "is_primary": true,
      "skill_name": "Security"
    },
    {
      "is_primary": true,
      "skill_name": "Reliability"
    },
    {
      "is_primary": true,
      "skill_name": "Efficiency"
    }
  ],
  "jd_role": {
    "display_name": "LLM Operations Engineer",
    "rationale": null,
    "role_archetype": "Other",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": null,
    "certifications": [],
    "company_name": null,
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [],
        "domain": "Other"
      },
      "secondary": null
    },
    "education": [
      {
        "level": "null",
        "qualification": "null - null",
        "raw": "15 years full time education",
        "requirement": "required"
      }
    ],
    "experience": {
      "max": null,
      "min": 3,
      "raw": "Minimum 3 Year(s) Of Experience Is Required"
    },
    "job_locations": [
      {
        "aliases": [
          "Gurgaon"
        ],
        "city": "Gurugram",
        "country": "India",
        "state": null,
        "work_mode": "null"
      }
    ],
    "role": "LLM Operations Engineer",
    "role_archetype": "Other",
    "roles_and_responsibilities": [
      {
        "bullet_count": 6,
        "heading": "Roles \u0026 Responsibilities",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Expected to perform independently and",
          "last_5_words": "guidance and sharing expertise to"
        },
        "text": "Expected to perform independently and become an SME.\nRequired active participation/contribution in team discussions.\nContribute in providing solutions to work related problems.\nCollaborate with cross-functional teams to optimize deployment workflows and operational processes.\nAssist in documenting operational procedures and best practices to support knowledge sharing.\nSupport junior team members by providing guidance and sharing expertise to foster professional growth.",
        "word_count": 56
      },
      {
        "bullet_count": 5,
        "heading": "Professional \u0026 Technical Skills",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "Must To Have Skills: Proficiency",
          "last_5_words": "for reliability and efficiency."
        },
        "text": "Must To Have Skills: Proficiency in Large Language Models (LLMs).\nExperience with cloud-native services and tools for deployment and monitoring of AI models.\nStrong understanding of operational challenges related to AI systems and strategies to address them.\nFamiliarity with compliance and security standards applicable to AI and data operations.\nAbility to analyze system performance metrics and implement improvements for reliability and efficiency.",
        "word_count": 66
      }
    ],
    "urls": []
  },
  "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": 3431,
      "existing_alias_text": "AI",
      "input_term": "AI",
      "matched_canonical": {
        "category_id": 2,
        "display_name": "AI",
        "id": 2634,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "ai",
        "sub_category_id": 2147,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    }
  ],
  "candidate_roles": [
    {
      "display_name": "Network Engineer",
      "id": 21,
      "rationale": null,
      "role_archetype": null,
      "slug": "network-engineer",
      "source": "db"
    },
    {
      "display_name": "Virtualization Engineer",
      "id": 26,
      "rationale": null,
      "role_archetype": null,
      "slug": "virtualization-engineer",
      "source": "db"
    }
  ],
  "chosen_role": {
    "display_name": "LLM Operations Engineer",
    "id": null,
    "rationale": "The primary skills focus on AI and cloud technologies, which align with the LLM operations role.",
    "role_archetype": "Other",
    "slug": "llm-operations-engineer",
    "source": "llm"
  },
  "dimensions": [
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Version Control Systems",
        "id": 365,
        "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
        "slug": "d_init_01",
        "source": "db"
      },
      "input_skill": "AI",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Version Control Systems",
        "id": 365,
        "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
        "slug": "d_init_01",
        "source": "db"
      },
      "input_skill": "Large Language Models",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Version Control Systems",
        "id": 365,
        "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
        "slug": "d_init_01",
        "source": "db"
      },
      "input_skill": "Cloud Native",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Version Control Systems",
        "id": 365,
        "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
        "slug": "d_init_01",
        "source": "db"
      },
      "input_skill": "Security",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Version Control Systems",
        "id": 365,
        "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
        "slug": "d_init_01",
        "source": "db"
      },
      "input_skill": "Reliability",
      "llm_role": null,
      "roles_from_db": []
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Capacity Planning and Performance Tuning",
        "id": 284,
        "rationale": "Forecasting and tuning the virtualization estate to maintain headroom and acceptable performance. This includes analyzing contention, balancing density, and responding to growth trends.",
        "slug": "capacity-planning-and-performance-tuning",
        "source": "db"
      },
      "input_skill": "Efficiency",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Network Engineer",
          "id": 21,
          "rationale": null,
          "role_archetype": null,
          "slug": "network-engineer",
          "source": "db"
        },
        {
          "display_name": "Virtualization Engineer",
          "id": 26,
          "rationale": null,
          "role_archetype": null,
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            "display_name": "Capacity Planning and Performance Tuning",
            "id": 284,
            "rationale": "Forecasting and tuning the virtualization estate to maintain headroom and acceptable performance. This includes analyzing contention, balancing density, and responding to growth trends.",
            "slug": "capacity-planning-and-performance-tuning",
            "source": "db"
          },
          "input_skill": "Efficiency",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Network Engineer",
              "id": 21,
              "rationale": null,
              "role_archetype": null,
              "slug": "network-engineer",
              "source": "db"
            },
            {
              "display_name": "Virtualization Engineer",
              "id": 26,
              "rationale": null,
              "role_archetype": null,
              "slug": "virtualization-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Version Control Systems",
            "id": 365,
            "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "Efficiency",
          "llm_role": null,
          "roles_from_db": []
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Capacity Planning and Performance Tuning",
            "id": 284,
            "rationale": "Forecasting and tuning the virtualization estate to maintain headroom and acceptable performance. This includes analyzing contention, balancing density, and responding to growth trends.",
            "slug": "capacity-planning-and-performance-tuning",
            "source": "db"
          },
          "input_skill": "Efficiency",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Network Engineer",
              "id": 21,
              "rationale": null,
              "role_archetype": null,
              "slug": "network-engineer",
              "source": "db"
            },
            {
              "display_name": "Virtualization Engineer",
              "id": 26,
              "rationale": null,
              "role_archetype": null,
              "slug": "virtualization-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Efficiency",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Concept",
          "skill_nature": "CONCEPT",
          "sub_category": "efficiency_principle",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "NOT_APPLICABLE",
          "volatility": "STABLE"
        },
        "enrichment": {
          "ambiguity": {
            "ambiguity_flag": true,
            "confused_with": [
              "efficiency_ratio",
              "energy_efficiency"
            ],
            "reasoning": "\"Efficiency\" is a broad concept and in JDs could refer to efficiency ratio or energy efficiency rather than the general performance-tuning principle, so an extractor may conflate it with related catalog skills."
          },
          "context_keywords": {
            "context_keywords": [
              "throughput",
              "latency",
              "resource utilization",
              "bottleneck analysis",
              "profiling",
              "benchmarking",
              "capacity planning",
              "performance tuning",
              "scalability",
              "load testing",
              "optimization",
              "CPU utilization",
              "memory footprint",
              "response time"
            ]
          },
          "maturity": {
            "confidence": 0.93,
            "maturity": "well_known",
            "reasoning": "Efficiency is a core performance-tuning principle referenced across most engineering JDs and capacity-planning roles; it\u2019s a standard expectation in system design, SRE, and backend interviews rather than a niche tool or sunset practice."
          },
          "skill_id": "efficiency",
          "vendor_license": {
            "confidence": 0.99,
            "license": null,
            "vendor": null,
            "year_introduced": null
          },
          "versioning": {
            "current_version": null,
            "version_aliases": {},
            "versioned": false
          }
        },
        "keep_log": [
          {
            "a_dim_id": "capacity-planning-and-performance-tuning",
            "a_name": "Capacity Planning and Performance Tuning",
            "a_role": "__skill_focal__",
            "b_dim_id": "capacity-planning-and-performance-tuning",
            "b_name": "Capacity Planning and Performance Tuning",
            "b_role": "Virtualization Engineer",
            "pair_kind": "cross_role",
            "reasoning": "Dim A is general system/application performance work: throughput, latency, resource utilization, workload right-sizing, bottleneck analysis, and tuning compute/memory/storage/network budgets. Dim B is virtualization-specific: forecasting and tuning the virtualization estate, maintaining headroom, analyzing contention, and balancing density. A\u2019s exemplars like throughput tuning and latency tuning fit broad runtime optimization; B\u2019s focus on estate density and headroom is a different layer of infrastructure management. Same label, different skill cluster.",
            "similarity": 0.7154335963486582
          },
          {
            "a_dim_id": "capacity-planning-and-performance-tuning",
            "a_name": "Capacity Planning and Performance Tuning",
            "a_role": "__skill_focal__",
            "b_dim_id": "performance-tuning-and-latency",
            "b_name": "Performance Tuning and Latency",
            "b_role": "Storage Engineer",
            "pair_kind": "cross_role",
            "reasoning": "Dim A is broad system-level capacity/performance work: it covers throughput, latency, resource utilization, workload right-sizing, headroom analysis, and density optimization across compute/memory/storage/network. Its exemplars include capacity forecasting, resource utilization, bottleneck analysis, and workload right-sizing. Dim B is storage-specific: it targets storage throughput, IOPS, and latency under workload pressure, with changes to media, protocol, and configuration. The overlap is only in generic performance wording; the underlying skill clusters differ.",
            "similarity": 0.7080801755296889
          }
        ],
        "locked_dimensions": [
          {
            "description": "Covers improving system throughput, latency, and resource utilization by sizing workloads, removing bottlenecks, and tuning configurations. Efficiency fits here when it means getting more useful work from the same compute, memory, storage, or network budget.",
            "exemplar_skills": [
              "Efficiency",
              "capacity forecasting",
              "resource utilization",
              "bottleneck analysis",
              "workload right-sizing",
              "throughput tuning",
              "latency tuning"
            ],
            "in_scope": "Efficiency, capacity forecasting, headroom analysis, contention reduction, workload right-sizing, throughput tuning, latency tuning, resource utilization, bottleneck analysis, performance baselining, density optimization",
            "name": "Capacity Planning and Performance Tuning",
            "out_of_scope": "Code readability, feature design, and product UX efficiency; those belong to application or frontend dimensions. Security hardening and incident handling are excluded because they optimize risk response rather than runtime efficiency.",
            "overlap_flags": [
              {
                "reason": "Both dimensions address runtime speed and bottlenecks; this one is broader and includes capacity/headroom concerns.",
                "with_dim_id": "performance-tuning-and-latency",
                "with_dim_name": null,
                "with_role": "Storage Engineer"
              },
              {
                "reason": "Query-level efficiency can be a subcase of overall performance tuning, especially for data-heavy systems.",
                "with_dim_id": "data-access-and-query-optimization",
                "with_dim_name": null,
                "with_role": "Data Engineer"
              }
            ],
            "tentative_id": "capacity-planning-and-performance-tuning"
          },
          {
            "description": "Covers choosing and analyzing approaches that reduce time or space complexity in code and data processing. Efficiency belongs here when it refers to making an implementation faster, smaller, or less computationally expensive.",
            "exemplar_skills": [
              "Efficiency",
              "time complexity",
              "space complexity",
              "algorithm optimization",
              "asymptotic analysis",
              "memoization",
              "data structure choice"
            ],
            "in_scope": "Efficiency, time complexity, space complexity, algorithm optimization, asymptotic analysis, memoization, batching, caching, computational cost reduction, data structure choice",
            "name": "Algorithmic Efficiency",
            "out_of_scope": "Infrastructure sizing, cluster utilization, and hardware tuning are excluded because they belong to capacity and performance operations. UI responsiveness and visual rendering efficiency are excluded because they belong to frontend performance dimensions.",
            "overlap_flags": [
              {
                "reason": "Efficiency may appear in scripting and notebook work, but this dimension is about the computational properties of the solution rather than the language itself.",
                "with_dim_id": "analytical-programming-languages",
                "with_dim_name": null,
                "with_role": "Data Analyst, Data Scientist"
              }
            ],
            "tentative_id": "d_init_01"
          },
          {
            "description": "Forecasting and tuning the virtualization estate to maintain headroom and acceptable performance. This includes analyzing contention, balancing density, and responding to growth trends.",
            "exemplar_skills": [
              "Capacity Planning and Performance Tuning"
            ],
            "in_scope": "Skills, tools, and practices that belong under Capacity Planning and Performance Tuning for the target role, including items implied by the dimension rationale.",
            "name": "Capacity Planning and Performance Tuning",
            "out_of_scope": "Adjacent clusters explicitly not owned by Capacity Planning and Performance Tuning, including unrelated platforms, roles, and skill families per library policy.",
            "overlap_flags": [],
            "tentative_id": "capacity-planning-and-performance-tuning"
          }
        ],
        "merge_log": [],
        "placed": {
          "name": "Efficiency",
          "placement_confidence": 0.92,
          "primary_dimension": "capacity-planning-and-performance-tuning",
          "reasoning": "Deterministic JD placement: locked_dimensions has 3 dimension(s) from skill-driven dimension generation after reconciliation; primary_dimension is the first locked dim.",
          "secondary_dimensions": [
            "d_init_01"
          ],
          "skill_id": "efficiency"
        },
        "relationships": {
          "child_skills": [],
          "parent_skills": [],
          "related_to": [
            "evaluation",
            "prompt-engineering",
            "agile",
            "observability",
            "testability",
            "workflow-automation",
            "queueing",
            "devops",
            "lean",
            "algorithms"
          ],
          "requires": [],
          "skill_id": "efficiency",
          "suppress_on_match": []
        },
        "skill_id": "efficiency",
        "split_log": [],
        "typed": {
          "alternatives_considered": [],
          "confidence": 0.93,
          "name": "Efficiency",
          "reasoning": "Efficiency is a named knowledge unit about minimizing resource use or waste, so by the Concept vs Methodology rule it is a Concept rather than a way of working.",
          "skill_id": "efficiency",
          "subtype": "efficiency_principle",
          "type": "Concept"
        },
        "warnings": [
          "stage3_post_filter_dropped_catalog_only_locked_dims:42-\u003e3"
        ]
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Large Language Models",
    "Cloud Native",
    "Security",
    "Reliability",
    "Efficiency"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "LLM Operations Engineer",
    "id": null,
    "rationale": "The primary skills focus on AI and cloud technologies, which align with the LLM operations role.",
    "role_archetype": "Other",
    "slug": "llm-operations-engineer",
    "source": "llm"
  },
  "chosen_role_resolution": "human_review_required",
  "final_input_skills": [
    {
      "skill": "Large Language Models",
      "tag": "new"
    },
    {
      "skill": "Cloud Native",
      "tag": "new"
    },
    {
      "skill": "AI",
      "tag": "in_db"
    },
    {
      "skill": "Security",
      "tag": "new"
    },
    {
      "skill": "Reliability",
      "tag": "new"
    },
    {
      "skill": "Efficiency",
      "tag": "new"
    }
  ],
  "persistence": {
    "items": [
      {
        "chosen_role_id": null,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Version Control Systems",
          "id": 365,
          "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 365,
        "input_skill": "AI",
        "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": 2634,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": null,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Version Control Systems",
          "id": 365,
          "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 365,
        "input_skill": "Large Language Models",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 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": 2695,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": null,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Version Control Systems",
          "id": 365,
          "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 365,
        "input_skill": "Cloud Native",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 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": 2696,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": null,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Version Control Systems",
          "id": 365,
          "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 365,
        "input_skill": "Security",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 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": 2697,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": null,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Version Control Systems",
          "id": 365,
          "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 365,
        "input_skill": "Reliability",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 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": 2698,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": null,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Capacity Planning and Performance Tuning",
          "id": 284,
          "rationale": "Forecasting and tuning the virtualization estate to maintain headroom and acceptable performance. This includes analyzing contention, balancing density, and responding to growth trends.",
          "slug": "capacity-planning-and-performance-tuning",
          "source": "db"
        },
        "dimension_id": 284,
        "input_skill": "Efficiency",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Network Engineer",
            "id": 21,
            "rationale": null,
            "role_archetype": null,
            "slug": "network-engineer",
            "source": "db"
          },
          {
            "display_name": "Virtualization Engineer",
            "id": 26,
            "rationale": null,
            "role_archetype": null,
            "slug": "virtualization-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 2699,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": null,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Version Control Systems",
          "id": 365,
          "rationale": "Tools and workflows for tracking source changes, branching, merging, and collaborating on code history. Git belongs here because it is the canonical distributed version control system used to manage revisions and coordinate team development.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 365,
        "input_skill": "Efficiency",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "New skill saved \u00b7 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": 2699,
        "skill_tag": "in_db",
        "skipped_reason": null
      }
    ],
    "new_skills_created": 5,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 6,
    "skipped": 0
  },
  "planner_output": null,
  "run_id": "afe8eb6e-990a-4496-8b36-09eea3b1a6d1"
}

LLM Calls

Every model call made for this run, in pipeline order. Click a card to see the model's response.

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