Pipeline run
afe8eb6e-990a-4496-8b36-09eea3b1a6d1
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
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.
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.
Skill enrichment (orchestrator / LLM)
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.
(0.99)
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.
Not versioned
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.
- 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) |
Skill enrichment (orchestrator / LLM)
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.
(0.98)
“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.
Not versioned
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.
- 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) |
Aliases — catalog
- NgRx (CANONICAL) primary
- ngrx 17 (VERSION)
- ngrx v17 (VERSION)
- ngrx17 (VERSION)
- ngrx@17 (VERSION)
Context tags (catalog)
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) |
Skill enrichment (orchestrator / LLM)
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.
(0.99)
“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.
Not versioned
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.
- 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) |
Skill enrichment (orchestrator / LLM)
Reliability is a standard SRE/DevOps hiring requirement; job postings commonly ask for SLIs/SLOs, incident response, and high-availability design across cloud platforms.
(0.99)
“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.
Not versioned
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.
- 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) |
Skill enrichment (orchestrator / LLM)
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.
(0.99)
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.
Not versioned
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.
- 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
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,
"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_final_skills": [
"Large Language Models",
"Cloud Native",
"AI",
"Security",
"Reliability",
"Efficiency"
],
"input_llm_skills": [
"Large Language Models",
"Cloud Native",
"AI",
"Security",
"Reliability",
"Efficiency"
],
"new_aliases_persisted": 0,
"run_id": "afe8eb6e-990a-4496-8b36-09eea3b1a6d1",
"skills_detail": [
{
"aliases_in_db": [],
"canonical": null,
"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": "Large Language Models",
"llm_role": null,
"roles_from_db": []
}
],
"input_skill": "Large Language Models",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Concept",
"skill_nature": "CONCEPT",
"sub_category": "machine_learning_model_concept",
"typical_lifespan": "EVERGREEN",
"version_strategy": "NOT_APPLICABLE",
"volatility": "STABLE"
},
"enrichment": {
"ambiguity": {
"ambiguity_flag": false,
"confused_with": [],
"reasoning": "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."
},
"context_keywords": {
"context_keywords": [
"transformer",
"attention",
"tokenization",
"prompt engineering",
"fine-tuning",
"inference",
"embedding",
"context window",
"RAG",
"instruction tuning",
"RLHF",
"few-shot",
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"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.