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Pipeline run

fa7f45ee-ec59-418f-bfa9-a6c75709fdc1

Pipeline LLM cost (USD)
API 1: $0.0084 API 2: $0.0004 API 3: $0.0000 Total: $0.0089

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work · Deployment and Automation
Build and maintain ML deployment automation: set up CI/CD for model releases, optimize infrastructure and data pipelines for training/serving, and monitor model/system health to catch and fix issues. Also coordinate with DevOps, data science, and engineering on secure, maintainable releases.
"“Establish CI/CD pipelines for end-to-end automation of model updates and releases.”"
Tech stack maturity
Modern Cloud Native
An MLOps Engineer centered on CI/CD, DevOps, and machine learning most commonly operates in cloud-native environments with automated deployment and infrastructure practices.
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): MLOps, ML, Machine Learning
Evidence — skills matched in JD (16)
CI/CD DevOps Machine Learning Data Pipelines Version Control Code Quality Data Ingestion Preprocessing Feature Engineering Model Serving Model Monitoring Model Deployment Model Training Performance Tuning Security Protocols Compliance Standards
Skill cluster (3 dimension groups, role-scoped)
AI Governance and Model Security
Machine Learning
CI/CD Pipeline Platforms
DevOps
Cross-cutting / unaligned
CI/CD Data Pipelines Version Control Code Quality Data Ingestion Preprocessing Feature Engineering Model Serving Model Monitoring Model Deployment Model Training Performance Tuning Security Protocols Compliance Standards
Show KRA description ↓
• **Deployment and Automation:** • Design and implement automated workflows for model deployment and monitoring. • Establish CI/CD pipelines for end-to-end automation of model updates and releases. • **Infrastructure Management:** • Collaborate with DevOps and IT teams to optimize infrastructure for machine learning workloads. • Implement scalable solutions for data storage, processing, and model serving. • Develop and optimize workflows for model training, feature engineering, data ingestion, and preprocessing to enable efficient and reproducible machine learning processes. • **Monitoring and Maintenance:** • Develop and maintain monitoring systems to track model performance and system health. • Implement proactive measures for identifying and resolving potential issues. • Work closely with cross-functional teams to identify and resolve operational challenges related to data pipelines, infrastructure, and performance tuning. • **Collaboration:** • Work closely with data scientists, software engineers, and other stakeholders to understand model requirements and integration points. • Provide guidance on best practices for code quality, version control, and deployment strategies to ensure the stability and maintainability of machine learning systems. • **Security and Compliance:** • Ensure security protocols are in place for protecting sensitive data and models. • Stay updated on industry regulations and compliance standards related to machine learning.

Signals

Skill ml-engineer
0.40
Alias ml-ops-engineer
1.00
KRA ml-engineer
0.64

Post-classification

Centroidupdated · n=9
Alias collision log
New-role queue
New skills captured12
New KRA captured

Captured for admin review

Data Pipelines primary MLOps Engineer pending
Version Control primary MLOps Engineer pending
Data Ingestion MLOps Engineer pending
Preprocessing MLOps Engineer pending
Feature Engineering MLOps Engineer pending
Model Serving MLOps Engineer pending
Model Monitoring MLOps Engineer pending
Model Deployment MLOps Engineer pending
Model Training MLOps Engineer pending
Performance Tuning MLOps Engineer pending
Security Protocols MLOps Engineer pending
Compliance Standards MLOps Engineer pending
Status: completed Created: 2026-05-27T16:17:15.349001Z Updated: 2026-05-27T16:18:53.074762Z API 3 duration: 27125 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

MLOps Engineer

domain · AI / ML CASE DOMAIN

slug: ml-ops-engineer · id: 16 · source: db

Domain=AI / ML; The JD centers on deployment automation, CI/CD, monitoring, infrastructure for ML workloads, and model operations, which closely matches MLOps Engineer.

Matched skills

model deploymentmonitoringCI/CD pipelinesDevOpsIT teamsdata storageprocessingmodel servingmodel trainingfeature engineeringdata ingestionpreprocessingversion controldeployment strategiessecurity protocols

Matched dimensions

MLOps automationML infrastructure optimizationModel monitoring and maintenanceML workflow engineeringCross-functional operational supportSecurity and compliance

Matched KRAs

Design and implement automated workflows for model deployment and monitoringEstablish CI/CD pipelines for end-to-end automationOptimize infrastructure for machine learning workloadsImplement scalable solutions for data storage, processing, and model servingDevelop and optimize workflows for model training, feature engineering, data ingestion, and preprocessingDevelop and maintain monitoring systems to track model performance and system healthImplement proactive measures for identifying and resolving potential issuesWork closely with cross-functional teams to identify and resolve operational challengesProvide guidance on best practices for code quality, version control, and deployment strategiesEnsure security protocols are in place for protecting sensitive data and models

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
1
Skipped

Job description

We are seeking a talented and experienced MLOps engineer to join our dynamic team. As an MLOps engineer, you will be the architect of seamless integration between machine learning models and production systems. Your role is pivotal in ensuring the reliability, scalability, and efficiency of our machine-learning infrastructure

• Suitable person has hand-on experience with a large scale Enterprise Data Lake (EDL) setup in AWS Cloud using Serverless and standard AWS Data Lake Architecture
• Should have dealt with EDL connectors for SFDC, Snowflake, Oracle and other third party systems
• Need to be familiar with Job monitoring, diagnostics, issue resolution, scalability& performance tuning
• Should be able to suggest and design resilient and scalable pipelines for data ingestion and ML Training models
• Should be well versed in managing Production grade EDL deployments and best practices related to data access, PII data protection, Access& Control, Data Lifecycle management
• 6-8 years’ experience as a Data Architect with experience in managing full lifecycle of Data Engineering and Cloud best practices for Enterprise grade deployments
• Good communication skills and proactive problem solver.
• Individual contributor role with some mentoring abilities
• Knowledge of Databricks or other tools good to have
• ITIL and Managed Services Support Model concepts


Responsibilities

• **Deployment and Automation:**
• Design and implement automated workflows for model deployment and monitoring.
• Establish CI/CD pipelines for end-to-end automation of model updates and releases.
• **Infrastructure Management:**
• Collaborate with DevOps and IT teams to optimize infrastructure for machine learning workloads.
• Implement scalable solutions for data storage, processing, and model serving.
• Develop and optimize workflows for model training, feature engineering, data ingestion, and preprocessing to enable efficient and reproducible machine learning processes.
• **Monitoring and Maintenance:**
• Develop and maintain monitoring systems to track model performance and system health.
• Implement proactive measures for identifying and resolving potential issues.
• Work closely with cross-functional teams to identify and resolve operational challenges related to data pipelines, infrastructure, and performance tuning.
• **Collaboration:**
• Work closely with data scientists, software engineers, and other stakeholders to understand model requirements and integration points.
• Provide guidance on best practices for code quality, version control, and deployment strategies to ensure the stability and maintainability of machine learning systems.
• **Security and Compliance:**
• Ensure security protocols are in place for protecting sensitive data and models.
• Stay updated on industry regulations and compliance standards related to machine learning.


Qualifications

• Bachelor’s or higher degree in Computer Science, Engineering, or a related field.
• Proven experience in deploying and maintaining machine learning models in production.
• Strong proficiency in scripting languages (e.g., Python) and experience with relevant frameworks (e.g., TensorFlow, PyTorch).
• Solid understanding of cloud platforms (AWS preferred) and containerization technologies (e.g., Docker, Kubernetes).
• Familiarity with CI/CD tools and version control systems.


Skills

• Automation and scripting skills for building scalable MLOps workflows.
• Strong problem-solving and troubleshooting abilities.
• Excellent collaboration and communication skills.
• Knowledge of best practices in model monitoring, logging, and debugging.

Skills from this JD

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

CI/CD Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: CI/CD id=1190 · ci-cd

Aliases — catalog

  • CI/CD (CANONICAL)

Context tags (catalog)

Ansible CircleCI Docker GitLab CI Jenkins Kubernetes Terraform Travis CI automated testing build automation continuous deployment continuous integration deployment pipelines monitoring version control

Stored enrichment (catalog DB)

Category
Methodology
Sub-category
Ci Cd Process
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: CI/CD appears in a large share of software engineering JDs and is a standard requirement across DevOps, platform, and backend roles; major vendors like GitHub, GitLab, and AWS all center product roadmaps on CI/CD pipelines.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
900
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • CI/CD Pipeline Platforms Catalog dimension db id 150

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

  • CI/CD for Machine Learning Catalog dimension db id 56

    Library dimension (catalog)

    Roles linked in library: ML Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
DevOps Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: DevOps id=1216 · devops

Aliases — catalog

  • DevOps (CANONICAL)

Context tags (catalog)

Agile Ansible Automation CI/CD Cloud-native Continuous Deployment Continuous Integration Docker GitOps Infrastructure as Code Jenkins Kubernetes Microservices Monitoring SRE Terraform

Stored enrichment (catalog DB)

Category
Methodology
Sub-category
Devops Methodology
Confidence
0.97
Version strategy
NOT_APPLICABLE

Maturity reasoning: DevOps appears in a large share of software and platform engineering job descriptions, often alongside CI/CD, Kubernetes, and cloud tooling; it is a standard hiring-pipeline keyword rather than a niche specialty.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
922
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • CI/CD Pipeline Platforms Catalog dimension db id 150

    Library dimension (catalog)

    Roles linked in library: DevOps Engineer

  • Deployment and Release Patterns Catalog dimension db id 140

    Library dimension (catalog)

    Roles linked in library: Cloud Architect

  • Infrastructure as Code Catalog dimension db id 132

    Library dimension (catalog)

    Roles linked in library: Cloud Architect, DevOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Deployment and Release Patterns
deployment-and-release-patterns
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Infrastructure as Code
infrastructure-as-code
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Machine Learning Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Machine Learning id=1356 · machine-learning

Aliases — catalog

  • Machine Learning (CANONICAL)

Context tags (catalog)

Keras PyTorch TensorFlow cross-validation data preprocessing ensemble methods feature engineering hyperparameter tuning model evaluation natural language processing neural networks reinforcement learning scikit-learn supervised learning unsupervised learning

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Machine Learning
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Machine Learning appears in large volumes of job descriptions across data, product, and platform roles, and major cloud vendors (AWS, Google Cloud, Azure) offer dedicated ML services and certifications, indicating broad adoption.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1024
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • AI Governance and Model Security Catalog dimension db id 50

    Library dimension (catalog)

    Roles linked in library: AI Engineer, ML Engineer, MLOps Engineer

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
AI Governance and Model Security
ai-governance-and-model-security
Existing dimension (library) · Role↔dimension saved
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Data Pipelines 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
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Version Control 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
Software Development
Sub-category
general
Skill nature
PRACTICE
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
Code Quality Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: code quality id=1653 · code-quality

Aliases — catalog

  • code quality (CANONICAL)

Context tags (catalog)

CI/CD best practices code coverage code review design patterns documentation linting maintainability performance optimization refactoring static analysis technical debt test-driven development unit testing version control

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Code Quality
Confidence
0.94
Version strategy
NOT_APPLICABLE

Maturity reasoning: Code quality is a standard hiring-pipeline expectation in JDs for software engineers and is reinforced by widespread use of linters, code review, and CI quality gates across major repos and platforms.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1246
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)
Data Ingestion Secondary 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
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Preprocessing Secondary 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
Data Engineering Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Feature Engineering Secondary 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
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Model Serving Secondary 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
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Model Monitoring Secondary 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
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Model Deployment Secondary 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
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Model Training Secondary 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
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Performance Tuning Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: query tuning id=3553 · query-tuning

Aliases — catalog

  • query tuning (CANONICAL) primary
  • Query Tuning (CANONICAL)

Context tags (catalog)

SQL SQL optimization caching cost estimation cost-based optimization data retrieval database indexing database optimization database performance database profiling database statistics database tuning execution plan execution time indexing join strategies load balancing parameter sniffing query analysis query complexity query execution query performance query profiling query rewriting resource allocation resource utilization statistics

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Query Optimization
Confidence
0.82
Version strategy
NOT_APPLICABLE

Maturity reasoning: Common in DB/analytics job descriptions and vendor docs; PostgreSQL, MySQL, SQL Server, and Oracle all expose EXPLAIN/ANALYZE and tuning guides, showing broad hiring-pipeline demand.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
3067
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Relational Database Design Catalog dimension db id 4

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Kotlin Backend Developer, Node.js Backend Developer, Python Backend Developer, Ruby Backend Developer, Scala Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Relational Database Design
relational-database-design
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Security Protocols Secondary 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
Security Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Compliance Standards Secondary 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
Security Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED

All API 3 persistence rows

Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.

Skill Tag Dimension Skill↔dim Role↔dim Outcome Notes
CI/CD in_db
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
CI/CD in_db
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
DevOps in_db
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
DevOps in_db
Deployment and Release Patterns
deployment-and-release-patterns
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
DevOps in_db
Infrastructure as Code
infrastructure-as-code
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Machine Learning in_db
AI Governance and Model Security
ai-governance-and-model-security
Existing dimension (library) · Role↔dimension saved
Machine Learning in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Code Quality in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Performance Tuning new
Relational Database Design
relational-database-design
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed Data Pipelines | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Version Control | type=Software Development subtype=general nature=PRACTICE lifespan=EVERGREEN
canonical_skill_proposed Data Ingestion | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Preprocessing | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Feature Engineering | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Model Serving | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Model Monitoring | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Model Deployment | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Model Training | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Security Protocols | type=Security Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Compliance Standards | type=Security Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
dimension_skill_link_proposed Performance Tuning ↔ Relational Database Design
nano JD Parser — gpt-4.1-nano click to toggle
RoleMLOps engineer
Experience6-8 years’ experience as a Data Architect
DomainIT Services & Consulting
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [],
  "company_name": null,
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "ITES",
        "BPO"
      ],
      "domain": "IT Services \u0026 Consulting"
    },
    "secondary": null
  },
  "education": [
    {
      "level": "Bachelor\u0027s",
      "qualification": "Bachelor\u0027s - Computer Science / Engineering (or related)",
      "raw": "Bachelor\u2019s or higher degree in Computer Science, Engineering, or a related field.",
      "requirement": "required"
    }
  ],
  "experience": {
    "max": 8,
    "min": 6,
    "raw": "6-8 years\u2019 experience as a Data Architect"
  },
  "job_locations": [],
  "role": "MLOps engineer",
  "role_aliases": [
    "Machine Learning Operations Engineer",
    "MLOps Engineer"
  ],
  "role_archetype": "Engineering",
  "roles_and_responsibilities": [
    {
      "bullet_count": 15,
      "heading": "Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 **Deployment and Automation:**",
        "last_5_words": "related to machine learning."
      },
      "text": "\u2022 **Deployment and Automation:**\n\u2022 Design and implement automated workflows for model deployment and monitoring.\n\u2022 Establish CI/CD pipelines for end-to-end automation of model updates and releases.\n\u2022 **Infrastructure Management:**\n\u2022 Collaborate with DevOps and IT teams to optimize infrastructure for machine learning workloads.\n\u2022 Implement scalable solutions for data storage, processing, and model serving.\n\u2022 Develop and optimize workflows for model training, feature engineering, data ingestion, and preprocessing to enable efficient and reproducible machine learning processes.\n\u2022 **Monitoring and Maintenance:**\n\u2022 Develop and maintain monitoring systems to track model performance and system health.\n\u2022 Implement proactive measures for identifying and resolving potential issues.\n\u2022 Work closely with cross-functional teams to identify and resolve operational challenges related to data pipelines, infrastructure, and performance tuning.\n\u2022 **Collaboration:**\n\u2022 Work closely with data scientists, software engineers, and other stakeholders to understand model requirements and integration points.\n\u2022 Provide guidance on best practices for code quality, version control, and deployment strategies to ensure the stability and maintainability of machine learning systems.\n\u2022 **Security and Compliance:**\n\u2022 Ensure security protocols are in place for protecting sensitive data and models.\n\u2022 Stay updated on industry regulations and compliance standards related to machine learning.",
      "word_count": 263
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "CI/CD"
    },
    {
      "is_primary": true,
      "skill_name": "DevOps"
    },
    {
      "is_primary": true,
      "skill_name": "Machine Learning"
    },
    {
      "is_primary": true,
      "skill_name": "Data Pipelines"
    },
    {
      "is_primary": true,
      "skill_name": "Version Control"
    },
    {
      "is_primary": false,
      "skill_name": "Code Quality"
    },
    {
      "is_primary": false,
      "skill_name": "Data Ingestion"
    },
    {
      "is_primary": false,
      "skill_name": "Preprocessing"
    },
    {
      "is_primary": false,
      "skill_name": "Feature Engineering"
    },
    {
      "is_primary": false,
      "skill_name": "Model Serving"
    },
    {
      "is_primary": false,
      "skill_name": "Model Monitoring"
    },
    {
      "is_primary": false,
      "skill_name": "Model Deployment"
    },
    {
      "is_primary": false,
      "skill_name": "Model Training"
    },
    {
      "is_primary": false,
      "skill_name": "Performance Tuning"
    },
    {
      "is_primary": false,
      "skill_name": "Security Protocols"
    },
    {
      "is_primary": false,
      "skill_name": "Compliance Standards"
    }
  ],
  "jd_role": {
    "display_name": "MLOps engineer",
    "rationale": null,
    "role_aliases": [
      "Machine Learning Operations Engineer",
      "MLOps Engineer"
    ],
    "role_archetype": "Engineering",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": null,
    "certifications": [],
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}
API 2 — extract-details
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        "skill_id": "model-training",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "query tuning",
          "alias_type": "CANONICAL",
          "id": 5119,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        },
        {
          "alias_text": "Query Tuning",
          "alias_type": "CANONICAL",
          "id": 5584,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "query tuning",
        "id": 3553,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "query-tuning",
        "sub_category_id": 3067,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Relational Database Design",
            "id": 4,
            "rationale": "Modeling and operating relational persistence for backend services. Includes schema design, normalization, indexing, transactions, and query tuning for operational data stores.",
            "slug": "relational-database-design",
            "source": "db"
          },
          "input_skill": "Performance Tuning",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "Kotlin Backend Developer",
              "id": 84,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "kotlin-server-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Node.js Backend Developer",
              "id": 82,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "node-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Python Backend Developer",
              "id": 80,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "python-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Ruby Backend Developer",
              "id": 85,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "ruby-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Performance Tuning",
      "matched_via": "embedding_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": "Security Protocols",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Security Tools",
          "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": "security-protocols",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Compliance Standards",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Security Tools",
          "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": "compliance-standards",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Data Pipelines",
    "Version Control",
    "Data Ingestion",
    "Preprocessing",
    "Feature Engineering",
    "Model Serving",
    "Model Monitoring",
    "Model Deployment",
    "Model Training",
    "Security Protocols",
    "Compliance Standards"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "MLOps Engineer",
    "id": 16,
    "rationale": "Domain=AI / ML; The JD centers on deployment automation, CI/CD, monitoring, infrastructure for ML workloads, and model operations, which closely matches MLOps Engineer.",
    "role_archetype": null,
    "slug": "ml-ops-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "CI/CD",
      "tag": "in_db"
    },
    {
      "skill": "DevOps",
      "tag": "in_db"
    },
    {
      "skill": "Machine Learning",
      "tag": "in_db"
    },
    {
      "skill": "Data Pipelines",
      "tag": "new"
    },
    {
      "skill": "Version Control",
      "tag": "new"
    },
    {
      "skill": "Code Quality",
      "tag": "in_db"
    },
    {
      "skill": "Data Ingestion",
      "tag": "new"
    },
    {
      "skill": "Preprocessing",
      "tag": "new"
    },
    {
      "skill": "Feature Engineering",
      "tag": "new"
    },
    {
      "skill": "Model Serving",
      "tag": "new"
    },
    {
      "skill": "Model Monitoring",
      "tag": "new"
    },
    {
      "skill": "Model Deployment",
      "tag": "new"
    },
    {
      "skill": "Model Training",
      "tag": "new"
    },
    {
      "skill": "Performance Tuning",
      "tag": "in_db"
    },
    {
      "skill": "Security Protocols",
      "tag": "new"
    },
    {
      "skill": "Compliance Standards",
      "tag": "new"
    }
  ],
  "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": 16,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "CI/CD Pipeline Platforms",
          "id": 150,
          "rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
          "slug": "ci-cd-pipeline-platforms",
          "source": "db"
        },
        "dimension_id": 150,
        "input_skill": "CI/CD",
        "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": "DevOps Engineer",
            "id": 10,
            "rationale": null,
            "role_archetype": null,
            "slug": "devops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1190,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 16,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "CI/CD for Machine Learning",
          "id": 56,
          "rationale": "Tools and platforms for automating ML model integration, testing, and deployment pipelines.",
          "slug": "ci-cd-for-machine-learning",
          "source": "db"
        },
        "dimension_id": 56,
        "input_skill": "CI/CD",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1190,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 16,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "CI/CD Pipeline Platforms",
          "id": 150,
          "rationale": "Systems used to define, run, and maintain automated build and deployment workflows. This cluster is coherent because the role owns delivery automation end to end, including pipeline reliability and promotion logic.",
          "slug": "ci-cd-pipeline-platforms",
          "source": "db"
        },
        "dimension_id": 150,
        "input_skill": "DevOps",
        "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": "DevOps Engineer",
            "id": 10,
            "rationale": null,
            "role_archetype": null,
            "slug": "devops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1216,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 16,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Deployment and Release Patterns",
          "id": 140,
          "rationale": "Patterns for promoting changes safely across environments, including rollout, rollback, and release gating strategies. Cloud Architects define these patterns so teams can deploy consistently across the platform.",
          "slug": "deployment-and-release-patterns",
          "source": "db"
        },
        "dimension_id": 140,
        "input_skill": "DevOps",
        "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 Architect",
            "id": 9,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-architect",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1216,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 16,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Infrastructure as Code",
          "id": 132,
          "rationale": "Declarative provisioning and environment definition tools used to codify cloud infrastructure, repeatable environments, and platform standards. Cloud Architects use these to express reference architectures and guardrails.",
          "slug": "infrastructure-as-code",
          "source": "db"
        },
        "dimension_id": 132,
        "input_skill": "DevOps",
        "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 Architect",
            "id": 9,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-architect",
            "source": "db"
          },
          {
            "display_name": "DevOps Engineer",
            "id": 10,
            "rationale": null,
            "role_archetype": null,
            "slug": "devops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1216,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 16,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "AI Governance and Model Security",
          "id": 50,
          "rationale": "Controls and documentation used to make models safer, auditable, and compliant. ML engineers use this to manage model risk, supply chain integrity, and governance requirements.",
          "slug": "ai-governance-and-model-security",
          "source": "db"
        },
        "dimension_id": 50,
        "input_skill": "Machine Learning",
        "llm_role": null,
        "matched_chosen_role": true,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
        "role_dimension_saved": true,
        "roles_from_db": [
          {
            "display_name": "AI Engineer",
            "id": 13,
            "rationale": null,
            "role_archetype": null,
            "slug": "ai-engineer",
            "source": "db"
          },
          {
            "display_name": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
            "source": "db"
          },
          {
            "display_name": "MLOps Engineer",
            "id": 16,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-ops-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 1356,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 16,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "React Frontend Development",
          "id": 96,
          "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 96,
        "input_skill": "Machine Learning",
        "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": 1356,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 16,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "React Frontend Development",
          "id": 96,
          "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
          "slug": "d_init_01",
          "source": "db"
        },
        "dimension_id": 96,
        "input_skill": "Code Quality",
        "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": 1653,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 16,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Relational Database Design",
          "id": 4,
          "rationale": "Modeling and operating relational persistence for backend services. Includes schema design, normalization, indexing, transactions, and query tuning for operational data stores.",
          "slug": "relational-database-design",
          "source": "db"
        },
        "dimension_id": 4,
        "input_skill": "Performance Tuning",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Skipped \u2014 no persistable v3 meta for new skill",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": ".NET Backend Developer",
            "id": 83,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "dotnet-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Backend Developer",
            "id": 1,
            "rationale": null,
            "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
            "slug": "backend-engineer",
            "source": "db"
          },
          {
            "display_name": "Kotlin Backend Developer",
            "id": 84,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "kotlin-server-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Node.js Backend Developer",
            "id": 82,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "node-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Python Backend Developer",
            "id": 80,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "python-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Ruby Backend Developer",
            "id": 85,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "ruby-backend-developer",
            "source": "db"
          },
          {
            "display_name": "Scala Backend Developer",
            "id": 87,
            "rationale": null,
            "role_archetype": "Engineering",
            "slug": "scala-backend-developer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      }
    ],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 1
  },
  "planner_output": null,
  "run_id": "fa7f45ee-ec59-418f-bfa9-a6c75709fdc1"
}