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Pipeline run

47c76cc6-cdeb-4c5f-b69f-cec9f2b46da2

Pipeline LLM cost (USD)
API 1: $0.0081 API 2: $0.0003 API 3: $0.0000 Total: $0.0084

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work · AI/ML Application Architecture & MLOps
Build and prototype AI/ML analytics apps for grid automation, then package, deploy, and optimize them on edge/containerized systems with MLOps CI/CD and production data pipelines. Also define the data framework and work with product/R&D/regional teams to turn business problems into POCs.
""enable the ML Model end to end lifecycle""
Tech stack maturity
Modern Cloud Native
The role and primary skills center on AI infrastructure, MLOps, CI/CD, machine learning, and microservices, which strongly align with modern cloud-native engineering practices.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
3.20 / 5
Title match
Has AI skill
AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3): LLM, LLMs, MLOps, NLP, computer vision, AI, ML, AI/ML, Machine Learning, Deep Learning, Artificial Intelligence, Reinforcement Learning
Evidence — skills matched in JD (11)
Artificial Intelligence Machine Learning MLOps CI/CD Containerization Microservices Data Analytics Data Pipelines Databases GE GridNode Edge Computing
Skill cluster (3 dimension groups, role-scoped)
AI Governance and Model Security
Machine Learning
CI/CD for Machine Learning
MLOps
Cross-cutting / unaligned
Artificial Intelligence CI/CD Containerization Microservices Data Analytics Data Pipelines Databases GE GridNode Edge Computing
Show KRA description ↓
Artificial Intelligence (AI) Application architect is experienced in building grid innovation applications using model-to-code, and architecting prototype systems to validate and verify in containerized form. The AI application architect works in close collaboration with GA’s Innovation, PM/ R&D and regional teams in demonstration of innovation Apps in virtualized systems. This role will also be responsible for building the data analytics applications framework and work closely with other functions across Grid Automation (GA) business to identify areas where the business can leverage data and data analytics to drive efficiency, increase customer satisfaction, and develop POCs to solve critical problems for our customers. As part of the MLOps architecture development, the AI application architect will enable the ML Model end to end lifecycle i. e., from Commissioning training datasets to deploying it in production environment through automated CI/CD pipeline. • Demonstrate novel & transformational applications/analytics to drive innovation & differentiation. • Define the framework to collect, structure and use of databases for AI, to extract value. • Develop AI/ML application to build differentiated products and solutions; with ability to work on customers value-driven applications/analytics to drive innovations. • Design and deploy high-quality, scalable, and secure AI/ML models and applications on the GE GridNode/ edge platforms, using container or microservices principles. Develop and implement strategies for optimizing the performance and scalability of machine learning models in production. • Collaborate with product management, R&D, and other functions in to understand their needs and develop innovative solutions. Implement and maintain data pipelines for AI/ML models. Monitor and optimize the performance of AI/ML models in production. • Identification of Intellectual property/IP clearance. • Collaborate with cross-functional teams.

Signals

Skill ml-engineer
0.38
Alias ml-engineer
1.00
KRA ml-ops-engineer
0.63

Post-classification

Centroidupdated · n=2
Alias collision log
New-role queue
New skills captured6
New KRA capturedyes

Captured for admin review

Containerization primary AI Infrastructure Engineer pending
Data Analytics primary AI Infrastructure Engineer pending
Data Pipelines primary AI Infrastructure Engineer pending
Databases AI Infrastructure Engineer pending
GE GridNode AI Infrastructure Engineer pending
Edge Computing AI Infrastructure Engineer pending
R&R fragment (sim 0.00) AI Infrastructure Engineer pending

Artificial Intelligence (AI) Application architect is experienced in building grid innovation applications using model-to-code, and architecting prototype systems to validate and verify in containeriz…

Status: completed Created: 2026-05-27T14:04:43.310330Z Updated: 2026-05-27T14:06:07.511280Z API 3 duration: 17906 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

AI Infrastructure Engineer

domain · AI / ML CASE DOMAIN

slug: ai-infrastructure-engineer · id: 155 · source: db

Domain=AI / ML; The JD is centered on architecting AI applications, containerized prototype systems, data analytics frameworks, scalable edge deployments, and AI/ML architecture rather than pure model training or ops-only responsibilities.

Matched skills

model-to-codecontainerized formdata analytics applications frameworkMLOps architectureCI/CD pipelineAI/ML modelsGE GridNode/ edge platformscontainermicroservicesdata pipelines

Matched dimensions

AI application architecturePrototype system developmentData analytics platform designMLOps lifecycle enablementScalable and secure model deploymentEdge platform deploymentCross-functional innovation enablement

Matched KRAs

Building grid innovation applications using model-to-codeArchitecting prototype systems to validate and verifyBuild the data analytics applications frameworkEnable the ML Model end to end lifecycleDeploying it in production environment through automated CI/CD pipelineDefine the framework to collect, structure and use of databasesDesign and deploy high-quality, scalable, and secure AI/ML modelsImplement and maintain data pipelines for AI/ML modelsMonitor and optimize the performance of AI/ML models in production

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

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

Job description

Job Description Summary Artificial Intelligence (AI) Application architect is experienced in building grid innovation applications using model-to-code, and architecting prototype systems to validate and verify in containerized form. The AI application architect works in close collaboration with GA’s Innovation, PM/ R&D and regional teams in demonstration of innovation Apps in virtualized systems.

This role will also be responsible for building the data analytics applications framework and work closely with other functions across Grid Automation (GA) business to identify areas where the business can leverage data and data analytics to drive efficiency, increase customer satisfaction, and develop POCs to solve critical problems for our customers.

As part of the MLOps architecture development, the AI application architect will enable the ML Model end to end lifecycle i. e., from Commissioning training datasets to deploying it in production environment through automated CI/CD pipeline. Job Description

The AI application architect is responsible for

• Demonstrate novel & transformational applications/analytics to drive innovation & differentiation.
• Define the framework to collect, structure and use of databases for AI, to extract value.
• Develop AI/ML application to build differentiated products and solutions; with ability to work on customers value-driven applications/analytics to drive innovations.
• Design and deploy high-quality, scalable, and secure AI/ML models and applications on the GE GridNode/ edge platforms, using container or microservices principles. Develop and implement strategies for optimizing the performance and scalability of machine learning models in production.
• Collaborate with product management, R&D, and other functions in to understand their needs and develop innovative solutions. Implement and maintain data pipelines for AI/ML models. Monitor and optimize the performance of AI/ML models in production.
• Identification of Intellectual property/IP clearance.
• Collaborate with cross-functional teams.


Qualification/Requirements

• Master’s/PhD Degree in computer science, Information technology (IT), electrical engineering, or electric power engineering, specifically in the computer and electric power engineering field with minimum 6+ years of data science working experience.
• 6+ years of professional working environment and knowledge of artificial intelligence (AI) and machine learning (ML), including, unsupervised learning, supervised learning, and reinforcement learning, large language models (LLMs).
• 5-10 years R&D or Applications experience related to power system protection and automation.
• Proven experience in applying AI/ML frameworks/workflows, AI/MLOps with CI/CD using Cloud-native and on-prem development and deployment in OT/industrial automation environments.
• Hands-on professional experience in developing and testing AI/ML algorithms; AND/OR demonstrated professional experience with different scenarios of grid/physics models in power system simulation tools, MATLAB/PSCAD; as well as dynamics PSS/E, Digsilent, and equivalent.
• Experience with MLOps principles.
• Experience with DevOps, data pipelines, Azure ML registry, deployment methods viz. Docker, K8s, etc.
• Able to share ideas and work well in a team environment, proactive approach to tasks displaying initiative.
• Flexible and adaptable; open to change and modification of tasks, working in multi-tasking environment.
• Demonstrated professional experience with different scenarios of appropriate AI/ML models for energy/grid applications.


Desired Characteristics

• 6+ years of research or industry experience with simulation using scientific programming tools or languages, such as MATLAB, C++, C#, or Python, R, etc.
• 3 years of experience in developing and implementing ML models, such as predictive maintenance, load forecasting, and grid optimization using cloud servers such as AWS Sagemaker or equivalent in the Power Systems domain.
• 2 years of experience in a MLOps, data engineering, and cloud, working with real-time distribution grid data.
• Experience with Linux virtualized system deployment using VM, Hypervisor (EsXi, KVM, Xen etc.), Dockers and related tools.
• Experience as a system architect, team lead, industry recognized subject matter expert.
• Advanced experience in utilizing and applying common programming languages, such as Python, C/C++, Java, Spark and Hadoop, R Programming, Kafka, C#, MATLAB, along with good familiarity with power system modelling and data communication format.
• Expertise in Machine learning/Deep learning methods - LLM, NLP, Computer vision/ Image Processing.
• Expertise of GraphDB, SQL/NoSQL, MS Access, databases.
• Understanding/experience applying data analytics for Electrical Power System or industrial OT system.
• Understanding of GPU Experience, Spark, Scala for distributed computing.
• Strong root causing, trouble shooting and debugging skills using tools such as Wireshark, TCPDump and other Linux and Windows system tools.
• Strong communication skills and a proactive and open approach to conflict resolution.
• Strong organizational skills, self-motivated, and self-directed.
• Knowledge of modern protection and control and distribution automation developments and trends. 
• Proven record of writing and presenting papers at industry conferences/journals.


 Additional Information

Relocation Assistance Provided: Yes

Skills from this JD

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

Artificial Intelligence Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Artificial Intelligence id=1357 · artificial-intelligence

Aliases — catalog

  • Artificial Intelligence (CANONICAL)

Context tags (catalog)

AI ethics PyTorch TensorFlow algorithm optimization computer vision data mining deep learning machine learning model training natural language processing neural networks predictive analytics reinforcement learning supervised learning unsupervised learning

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Artificial Intelligence
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: AI appears in a large and growing share of job descriptions across software, data, and product roles, and major vendors (Microsoft, Google, AWS) have standardized AI offerings, signaling broad market adoption.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1020
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)
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 skipped (dimension not under chosen role)
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
MLOps Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: MLOps id=1196 · mlops

Aliases — catalog

  • MLOps (CANONICAL)

Context tags (catalog)

A/B testing CI/CD Docker Kubeflow Kubernetes MLflow automation cloud-native data governance data pipeline model deployment monitoring reproducibility scalability versioning

Stored enrichment (catalog DB)

Category
Methodology
Sub-category
Mlops
Confidence
0.93
Version strategy
NOT_APPLICABLE

Maturity reasoning: MLOps appears in many job descriptions for ML/platform roles and is a standard practice in major cloud vendor docs (AWS, GCP, Azure) for CI/CD, model monitoring, and deployment.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
906
Extractable
True
Also category
False

Dimensions (API 2 worklist)

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

    Library dimension (catalog)

    Roles linked in library: ML Engineer

  • Data Lineage and Metadata Catalog dimension db id 28

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Deployment Rollouts and Release Control Catalog dimension db id 51

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Data Lineage and Metadata
data-lineage-and-metadata
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Deployment Rollouts and Release Control
deployment-rollouts-and-release-control
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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)
Containerization 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
DevOps Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Microservices Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: microservices id=41 · microservices

Aliases — catalog

  • microservices (CANONICAL) primary

Context tags (catalog)

API Gateway API gateway CQRS DevOps Docker Kubernetes REST API RESTful services Saga pattern Spring Boot circuit breaker containerization decentralized distributed tracing domain-driven design event sourcing event-driven event-driven architecture gRPC load balancing message broker microservices patterns monitoring scalability service discovery service mesh

Stored enrichment (catalog DB)

Category
Architecture
Sub-category
Distributed System Architecture
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Microservices is a common architecture in job descriptions across backend/cloud roles, and major vendors like AWS, Google Cloud, and Kubernetes ecosystems provide first-class support and reference patterns.

Skill profile (library / DB)

Skill nature
PATTERN
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
1
Sub-category id
1
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Microservices and Distributed Systems Catalog dimension db id 9

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Node.js Backend Developer, Scala Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Microservices and Distributed Systems
microservices-and-distributed-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Data Analytics 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
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
Databases 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
Databases
Sub-category
general
Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Version strategy
UNVERSIONED
GE GridNode 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
Platform
Sub-category
general
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Edge Computing 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
Concepts
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
Artificial Intelligence in_db
React Frontend Development
d_init_01
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 skipped (dimension not under chosen role)
Machine Learning in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
MLOps in_db
CI/CD for Machine Learning
ci-cd-for-machine-learning
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
MLOps in_db
Data Lineage and Metadata
data-lineage-and-metadata
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
MLOps in_db
Deployment Rollouts and Release Control
deployment-rollouts-and-release-control
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
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)
Microservices in_db
Microservices and Distributed Systems
microservices-and-distributed-systems
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed Containerization | type=DevOps Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Data Analytics | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Data Pipelines | type=Data Engineering Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Databases | type=Databases subtype=general nature=TOOL lifespan=EVERGREEN
canonical_skill_proposed GE GridNode | type=Platform subtype=general nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Edge Computing | type=Concepts subtype=general nature=CONCEPT lifespan=MULTI_YEAR
nano JD Parser — gpt-4.1-nano click to toggle
RoleArtificial Intelligence (AI) Application Architect
Experienceminimum 6+ years of data science working experience
DomainEnergy & Utilities
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [],
  "company_name": null,
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "Electric Power",
        "Power Systems"
      ],
      "domain": "Energy \u0026 Utilities"
    },
    "secondary": null
  },
  "education": [
    {
      "level": "Master\u0027s",
      "qualification": "Master\u0027s/PhD - Computer Science / Information Technology / Electrical Engineering / Electric Power Engineering",
      "raw": "Master\u2019s/PhD Degree in computer science, Information technology (IT), electrical engineering, or electric power engineering",
      "requirement": "required"
    }
  ],
  "experience": {
    "max": null,
    "min": 6,
    "raw": "minimum 6+ years of data science working experience"
  },
  "job_locations": [],
  "role": "Artificial Intelligence (AI) Application Architect",
  "role_aliases": [
    "AI Architect",
    "AI Application Architect",
    "Machine Learning Architect"
  ],
  "role_archetype": "Engineering",
  "roles_and_responsibilities": [
    {
      "bullet_count": 0,
      "heading": "Role Overview",
      "heading_was_present": false,
      "source_marker": {
        "first_5_words": "Artificial Intelligence (AI) Application",
        "last_5_words": "through automated CI/CD pipeline."
      },
      "text": "Artificial Intelligence (AI) Application architect is experienced in building grid innovation applications using model-to-code, and architecting prototype systems to validate and verify in containerized form. The AI application architect works in close collaboration with GA\u2019s Innovation, PM/ R\u0026D and regional teams in demonstration of innovation Apps in virtualized systems.\n\nThis role will also be responsible for building the data analytics applications framework and work closely with other functions across Grid Automation (GA) business to identify areas where the business can leverage data and data analytics to drive efficiency, increase customer satisfaction, and develop POCs to solve critical problems for our customers.\n\nAs part of the MLOps architecture development, the AI application architect will enable the ML Model end to end lifecycle i. e., from Commissioning training datasets to deploying it in production environment through automated CI/CD pipeline.",
      "word_count": 139
    },
    {
      "bullet_count": 7,
      "heading": "The AI application architect is responsible for",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Demonstrate novel \u0026 transformational",
        "last_5_words": "with cross-functional teams."
      },
      "text": "\u2022 Demonstrate novel \u0026 transformational applications/analytics to drive innovation \u0026 differentiation.\n\u2022 Define the framework to collect, structure and use of databases for AI, to extract value.\n\u2022 Develop AI/ML application to build differentiated products and solutions; with ability to work on customers value-driven applications/analytics to drive innovations.\n\u2022 Design and deploy high-quality, scalable, and secure AI/ML models and applications on the GE GridNode/ edge platforms, using container or microservices principles. Develop and implement strategies for optimizing the performance and scalability of machine learning models in production.\n\u2022 Collaborate with product management, R\u0026D, and other functions in to understand their needs and develop innovative solutions. Implement and maintain data pipelines for AI/ML models. Monitor and optimize the performance of AI/ML models in production.\n\u2022 Identification of Intellectual property/IP clearance.\n\u2022 Collaborate with cross-functional teams.",
      "word_count": 164
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Artificial Intelligence"
    },
    {
      "is_primary": true,
      "skill_name": "Machine Learning"
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    {
      "is_primary": true,
      "skill_name": "MLOps"
    },
    {
      "is_primary": true,
      "skill_name": "CI/CD"
    },
    {
      "is_primary": true,
      "skill_name": "Containerization"
    },
    {
      "is_primary": true,
      "skill_name": "Microservices"
    },
    {
      "is_primary": true,
      "skill_name": "Data Analytics"
    },
    {
      "is_primary": true,
      "skill_name": "Data Pipelines"
    },
    {
      "is_primary": false,
      "skill_name": "Databases"
    },
    {
      "is_primary": false,
      "skill_name": "GE GridNode"
    },
    {
      "is_primary": false,
      "skill_name": "Edge Computing"
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  "jd_role": {
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    "slug": ""
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  "nano_parsed": {
    "JD_type": "pass",
    "about_company": null,
    "certifications": [],
    "company_name": null,
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [
          "Electric Power",
          "Power Systems"
        ],
        "domain": "Energy \u0026 Utilities"
      },
      "secondary": null
    },
    "education": [
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        "requirement": "required"
      }
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    "experience": {
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    "job_locations": [],
    "role": "Artificial Intelligence (AI) Application Architect",
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      "AI Architect",
      "AI Application Architect",
      "Machine Learning Architect"
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    "roles_and_responsibilities": [
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}
API 2 — extract-details
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      "matched_via": "alias"
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          "role_archetype": null,
          "slug": "devops-engineer",
          "source": "db"
        }
      ]
    },
    {
      "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"
      },
      "input_skill": "CI/CD",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "ML Engineer",
          "id": 3,
          "rationale": null,
          "role_archetype": null,
          "slug": "ml-engineer",
          "source": "db"
        }
      ]
    },
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "Microservices and Distributed Systems",
        "id": 9,
        "rationale": "Architectural patterns for decomposed backend systems and the operational concerns they introduce. Covers service boundaries, consistency tradeoffs, retries, circuit breakers, and distributed coordination.",
        "slug": "microservices-and-distributed-systems",
        "source": "db"
      },
      "input_skill": "Microservices",
      "llm_role": null,
      "roles_from_db": [
        {
          "display_name": "Backend Developer",
          "id": 1,
          "rationale": null,
          "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
          "slug": "backend-engineer",
          "source": "db"
        },
        {
          "display_name": "Node.js Backend Developer",
          "id": 82,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "node-backend-developer",
          "source": "db"
        },
        {
          "display_name": "Scala Backend Developer",
          "id": 87,
          "rationale": null,
          "role_archetype": "Engineering",
          "slug": "scala-backend-developer",
          "source": "db"
        }
      ]
    }
  ],
  "input_final_skills": [
    "Artificial Intelligence",
    "Machine Learning",
    "MLOps",
    "CI/CD",
    "Containerization",
    "Microservices",
    "Data Analytics",
    "Data Pipelines",
    "Databases",
    "GE GridNode",
    "Edge Computing"
  ],
  "input_llm_skills": [
    "Artificial Intelligence",
    "Machine Learning",
    "MLOps",
    "CI/CD",
    "Containerization",
    "Microservices",
    "Data Analytics",
    "Data Pipelines",
    "Databases",
    "GE GridNode",
    "Edge Computing"
  ],
  "new_aliases_persisted": 0,
  "run_id": "47c76cc6-cdeb-4c5f-b69f-cec9f2b46da2",
  "skills_detail": [
    {
      "aliases_in_db": [
        {
          "alias_text": "Artificial Intelligence",
          "alias_type": "CANONICAL",
          "id": 2016,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "Artificial Intelligence",
        "id": 1357,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "artificial-intelligence",
        "sub_category_id": 1020,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "Artificial Intelligence",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Artificial Intelligence",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Machine Learning",
          "alias_type": "CANONICAL",
          "id": 2015,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "Machine Learning",
        "id": 1356,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "machine-learning",
        "sub_category_id": 1024,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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"
          },
          "input_skill": "Machine Learning",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "AI Engineer",
              "id": 13,
              "rationale": null,
              "role_archetype": null,
              "slug": "ai-engineer",
              "source": "db"
            },
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            },
            {
              "display_name": "MLOps Engineer",
              "id": 16,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-ops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "Machine Learning",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Machine Learning",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "MLOps",
          "alias_type": "CANONICAL",
          "id": 1832,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 8,
        "display_name": "MLOps",
        "id": 1196,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "mlops",
        "sub_category_id": 906,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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"
          },
          "input_skill": "MLOps",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Data Lineage and Metadata",
            "id": 28,
            "rationale": "Cataloging, documenting, and tracing how data moves and changes across systems. This dimension supports impact analysis, governance, discoverability, and operational understanding of datasets.",
            "slug": "data-lineage-and-metadata",
            "source": "db"
          },
          "input_skill": "MLOps",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Deployment Rollouts and Release Control",
            "id": 51,
            "rationale": "Practices for safely promoting models through environments and managing rollback when production behavior changes. This dimension covers release gating, version pinning, and rollout strategies specific to ML systems.",
            "slug": "deployment-rollouts-and-release-control",
            "source": "db"
          },
          "input_skill": "MLOps",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            },
            {
              "display_name": "MLOps Engineer",
              "id": 16,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-ops-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "MLOps",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "CI/CD",
          "alias_type": "CANONICAL",
          "id": 1826,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 8,
        "display_name": "CI/CD",
        "id": 1190,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "ci-cd",
        "sub_category_id": 900,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "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"
          },
          "input_skill": "CI/CD",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "DevOps Engineer",
              "id": 10,
              "rationale": null,
              "role_archetype": null,
              "slug": "devops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "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"
          },
          "input_skill": "CI/CD",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "CI/CD",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Containerization",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "DevOps 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": "containerization",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "microservices",
          "alias_type": "CANONICAL",
          "id": 178,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 1,
        "display_name": "microservices",
        "id": 41,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PATTERN",
        "slug": "microservices",
        "sub_category_id": 1,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Microservices and Distributed Systems",
            "id": 9,
            "rationale": "Architectural patterns for decomposed backend systems and the operational concerns they introduce. Covers service boundaries, consistency tradeoffs, retries, circuit breakers, and distributed coordination.",
            "slug": "microservices-and-distributed-systems",
            "source": "db"
          },
          "input_skill": "Microservices",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "Node.js Backend Developer",
              "id": 82,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "node-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Microservices",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Analytics",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "PRACTICE",
          "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": "data-analytics",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Data Pipelines",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Data Engineering Tools",
          "skill_nature": "PRACTICE",
          "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": "data-pipelines",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Databases",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Databases",
          "skill_nature": "TOOL",
          "sub_category": "general",
          "typical_lifespan": "EVERGREEN",
          "version_strategy": "UNVERSIONED",
          "volatility": "STABLE"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "databases",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "GE GridNode",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Platform",
          "skill_nature": "PLATFORM",
          "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": "ge-gridnode",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Edge Computing",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Concepts",
          "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": "edge-computing",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Containerization",
    "Data Analytics",
    "Data Pipelines",
    "Databases",
    "GE GridNode",
    "Edge Computing"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "AI Infrastructure Engineer",
    "id": 155,
    "rationale": "Domain=AI / ML; The JD is centered on architecting AI applications, containerized prototype systems, data analytics frameworks, scalable edge deployments, and AI/ML architecture rather than pure model training or ops-only responsibilities.",
    "role_archetype": null,
    "slug": "ai-infrastructure-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Artificial Intelligence",
      "tag": "in_db"
    },
    {
      "skill": "Machine Learning",
      "tag": "in_db"
    },
    {
      "skill": "MLOps",
      "tag": "in_db"
    },
    {
      "skill": "CI/CD",
      "tag": "in_db"
    },
    {
      "skill": "Containerization",
      "tag": "new"
    },
    {
      "skill": "Microservices",
      "tag": "in_db"
    },
    {
      "skill": "Data Analytics",
      "tag": "new"
    },
    {
      "skill": "Data Pipelines",
      "tag": "new"
    },
    {
      "skill": "Databases",
      "tag": "new"
    },
    {
      "skill": "GE GridNode",
      "tag": "new"
    },
    {
      "skill": "Edge Computing",
      "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": 155,
        "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": "Artificial Intelligence",
        "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": 1357,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 155,
        "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": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "AI Engineer",
            "id": 13,
            "rationale": null,
            "role_archetype": null,
            "slug": "ai-engineer",
            "source": "db"
          },
          {
            "display_name": "ML Engineer",
            "id": 3,
            "rationale": null,
            "role_archetype": null,
            "slug": "ml-engineer",
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        "roles_from_db": [
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          "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.",
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          "rationale": "Architectural patterns for decomposed backend systems and the operational concerns they introduce. Covers service boundaries, consistency tradeoffs, retries, circuit breakers, and distributed coordination.",
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        "dimension_id": 9,
        "input_skill": "Microservices",
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          },
          {
            "display_name": "Node.js Backend Developer",
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          {
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        "skill_dimension_saved": true,
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}

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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