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Pipeline run

47f7a4fb-2dd3-48e9-9f81-30c213b8adb5

Pipeline LLM cost (USD)
API 1: $0.0031 API 2: $0.0002 API 3: $0.0000 Total: $0.0033

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work · platform engineering
Build and support Azure Machine Learning platform features, write tested code, troubleshoot live issues, and handle on-call plus security tool configuration/compliance remediation.
"drive the design, development, and support of the platform that powers Azure Machine Learning"
Tech stack maturity
Mainstream Modern
A cloud architect working with unit-testing aligns with established modern engineering practices that are widely used in contemporary software delivery.
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): Mistral, OpenAI, LLM, AI, Machine Learning, Artificial Intelligence
Evidence — skills matched in JD (6)
Azure Machine Learning Unit Testing Endpoint Security Log Collection Vulnerability Scanning Compliance Scanning
Skill cluster (1 dimension groups, role-scoped)
Cross-cutting / unaligned
Azure Machine Learning Unit Testing Endpoint Security Log Collection Vulnerability Scanning Compliance Scanning
Show KRA description ↓
As Software Engineer on our team, you will drive the design, development, and support of the platform that powers Azure Machine Learning. You’ll work across teams to help make the whole organization successful. Your responsibilities will include the following: • Write clean and concise code with unit tests.  • Design, implement, and support new features as well as extend existing systems.  • Investigate live site issues and implement and deploy fixes.  • Participate in an on-call rotation.  • Security Configuration and Compliance: Configure, update, and maintain security tools used for endpoint security, log collection and reporting, vulnerability, and compliance scanning. Hardening and compliance with best practices, benchmarks, and remediate vulnerabilities as reported by security.

Signals

Skill
Alias cloud-engineer
1.00
KRA cybersecurity-engineer
0.57

Post-classification

Centroidupdated · n=18
Alias collision log
New-role queue
New skills captured5
New KRA capturedyes

Captured for admin review

Azure Machine Learning primary Cloud Architect pending
Endpoint Security primary Cloud Architect pending
Log Collection primary Cloud Architect pending
Vulnerability Scanning primary Cloud Architect pending
Compliance Scanning primary Cloud Architect pending
R&R fragment (sim 0.00) Cloud Architect pending

As Software Engineer on our team, you will drive the design, development, and support of the platform that powers Azure Machine Learning. You’ll work across teams to help make the whole organization s…

Status: completed Created: 2026-05-27T14:50:39.902738Z Updated: 2026-06-12T17:10:50.657052Z API 3 duration: 4922 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

Cloud Architect

Cloud Engineer

sub-role · 1.00 CASE A

slug: cloud-architect · id: 9 · source: db · sub-role slug: cloud-engineer

Exact alias hit on cloud-engineer (1.0) — no other alias at this confidence; skill_top absent does not contradict

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

Microsoft bets on Artificial Intelligence (AI) as the next growth opportunity for the company. OpenAI, Mistral, and other large language Model (LLM) driven innovations are happening throughout the industry. Azure AI services group is focused on building a platform that makes it easy for both first party Microsoft teams and third-party customers to build cutting edge applications on top of these large language models.

The Pipelines team in Azure AI Platform is looking for a Software Engineer who loves to build a cloud service to help data scientists to author and process comprehensive machine learning multistep workflows.



The team focuses on:

• Managing microservices for pipeline authoring, scheduling and processing.  
• Secure Control Plane assets from malicious attacks and unauthorized access using industry standard tools and frameworks
• Automate Monitors and critical alerts using best in class observability tools such as: Azure Monitor, Prometheus, Azure Data Explorer, Grafana.  
• Automate CI/CD deployments using YAML builds and releases.


Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Responsibilities

Responsibilities:

As Software Engineer on our team, you will drive the design, development, and support of the platform that powers Azure Machine Learning. You’ll work across teams to help make the whole organization successful. Your responsibilities will include the following:

• Write clean and concise code with unit tests.  
• Design, implement, and support new features as well as extend existing systems.  
• Investigate live site issues and implement and deploy fixes.  
• Participate in an on-call rotation.  
• Security Configuration and Compliance: Configure, update, and maintain security tools used for endpoint security, log collection and reporting, vulnerability, and compliance scanning. Hardening and compliance with best practices, benchmarks, and remediate vulnerabilities as reported by security.


Qualifications

Required Qualifications:

• Bachelor’s degree in computer science, or related technical discipline. OR equivalent experience.  
• 3 + years’ experience in/with object-oriented design fundamentals.  
• 3 + years of experience with coding in one of C#, Go, Rust, Java, C or C++.


Preferred Qualifications

• Experience with improving service operations or engineering fundamentals.  
• Collaboration skills, team player, thrive to make a difference.  
• Understanding of Microservices architecture, K8s, NGINX, Observability (Logs, Metrics, etc..), Network Layer protocols is a plus.  
• Understanding of cloud service architecture. 
• Certifications: one or more of Certified Kubernetes Application Developer (CKAD), Certified Kubernetes Admin (CKA).


Other Requirements

Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

#IDCAIPlatformHiring

#Pipelines, Azure AI services

Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Microsoft is an equal opportunity employer. Consistent with applicable law, all qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Skills from this JD

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

Azure Machine Learning Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Azure ML id=212 · azure-ml

Aliases — catalog

  • Azure ML (CANONICAL) primary

Context tags (catalog)

AKS AutoML Azure Databricks Azure DevOps Azure Functions Azure Machine Learning ML Studio MLflow REST API SDK v2 TensorFlow automated ML compute cluster compute instance data labeling data preprocessing datastore designer endpoint deployment feature store hyperparameter tuning model deployment model monitoring model registry notebooks pipeline orchestration pipelines scikit-learn workspace

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Ml Platform
Vendor
Microsoft
License
proprietary
Year introduced
2018
Confidence
0.97
Version strategy
NOT_APPLICABLE

Maturity reasoning: Azure ML appears frequently in ML/DS job postings and Microsoft’s Azure AI portfolio, indicating broad enterprise adoption for model training and deployment on Azure.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
175
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • MLOps Platforms and Lifecycle Catalog dimension db id 43

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
MLOps Platforms and Lifecycle
mlops-platforms-and-lifecycle
Skipped — no persistable v3 meta for new skill
skill_not_in_db_v3_proposed
Unit Testing Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Unit Testing id=517 · unit-testing

Aliases — catalog

  • Unit Testing (CANONICAL)

Context tags (catalog)

JUnit NUnit TDD arrange-act-assert assertions code coverage fixtures mocking pytest regression stubs test cases test doubles test runner xUnit

Stored enrichment (catalog DB)

Category
Methodology
Sub-category
Testing Methodology
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Unit testing is a standard hiring requirement across software JDs and appears in mainstream frameworks/docs; GitHub and Stack Overflow usage remain consistently high, with no successor replacing it.

Skill profile (library / DB)

Skill nature
METHODOLOGY
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
8
Sub-category id
44
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)
Endpoint Security 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
Security Tools
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Log Collection 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
Monitoring Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Vulnerability Scanning 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
Security Tools
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Compliance Scanning 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
Security Tools
Sub-category
general
Skill nature
PRACTICE
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
Azure Machine Learning new
MLOps Platforms and Lifecycle
mlops-platforms-and-lifecycle
Skipped — no persistable v3 meta for new skill skill_not_in_db_v3_proposed
Unit Testing in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed Endpoint Security | type=Security Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Log Collection | type=Monitoring Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Vulnerability Scanning | type=Security Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Compliance Scanning | type=Security Tools subtype=general nature=PRACTICE lifespan=MULTI_YEAR
dimension_skill_link_proposed Azure Machine Learning ↔ MLOps Platforms and Lifecycle
nano JD Parser — gpt-4.1-nano click to toggle
RoleSoftware Engineer
CompanyMicrosoft
Experience3 + years’ experience in/with object-oriented design fundamentals.
DomainIT Services & Consulting
JD type pass

Certifications

Certified Kubernetes Application Developer (CKAD) Certified Kubernetes Admin (CKA)
Show raw JSON
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API 1 — extract-from-jd click to toggle
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        "role_slug": "cloud-architect",
        "skill_name": "Endpoint Security",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 11214,
        "role_display_name": "Cloud Architect",
        "role_slug": "cloud-architect",
        "skill_name": "Log Collection",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 11215,
        "role_display_name": "Cloud Architect",
        "role_slug": "cloud-architect",
        "skill_name": "Vulnerability Scanning",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 11216,
        "role_display_name": "Cloud Architect",
        "role_slug": "cloud-architect",
        "skill_name": "Compliance Scanning",
        "status": "pending"
      }
    ],
    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
    "v3_role_slug": null,
    "v3_run_id": null
  }
}
API 2 — extract-details
{
  "alias_matches": [
    {
      "alias_persist_skipped_reason": "TODO: REMOVE AFTER TESTING \u2014 alias DB write disabled",
      "alias_persisted": false,
      "existing_alias_id": 464,
      "existing_alias_text": "Azure ML",
      "input_term": "Azure Machine Learning",
      "matched_canonical": {
        "category_id": 9,
        "display_name": "Azure ML",
        "id": 212,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "azure-ml",
        "sub_category_id": 175,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "embedding_alias"
    },
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 865,
      "existing_alias_text": "Unit Testing",
      "input_term": "Unit Testing",
      "matched_canonical": {
        "category_id": 8,
        "display_name": "Unit Testing",
        "id": 517,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "unit-testing",
        "sub_category_id": 44,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "matched_via": "alias"
    }
  ],
  "candidate_roles": [
    {
      "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"
    }
  ],
  "chosen_role": {
    "display_name": "Cloud Architect",
    "id": 9,
    "rationale": "Exact alias hit on cloud-engineer (1.0) \u2014 no other alias at this confidence; skill_top absent does not contradict",
    "role_archetype": null,
    "slug": "cloud-architect",
    "source": "db"
  },
  "dimensions": [
    {
      "dimension": {
        "difficulty_hint": "well_known",
        "display_name": "MLOps Platforms and Lifecycle",
        "id": 43,
        "rationale": "End-to-end managed platforms used to train, deploy, register, and govern models across their lifecycle. This is the operational control plane for production ML workflows.",
        "slug": "mlops-platforms-and-lifecycle",
        "source": "db"
      },
      "input_skill": "Azure Machine Learning",
      "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"
        }
      ]
    },
    {
      "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": "Unit Testing",
      "llm_role": null,
      "roles_from_db": []
    }
  ],
  "input_final_skills": [
    "Azure Machine Learning",
    "Unit Testing",
    "Endpoint Security",
    "Log Collection",
    "Vulnerability Scanning",
    "Compliance Scanning"
  ],
  "input_llm_skills": [
    "Azure Machine Learning",
    "Unit Testing",
    "Endpoint Security",
    "Log Collection",
    "Vulnerability Scanning",
    "Compliance Scanning"
  ],
  "new_aliases_persisted": 0,
  "run_id": "47f7a4fb-2dd3-48e9-9f81-30c213b8adb5",
  "skills_detail": [
    {
      "aliases_in_db": [
        {
          "alias_text": "Azure ML",
          "alias_type": "CANONICAL",
          "id": 464,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 9,
        "display_name": "Azure ML",
        "id": 212,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "azure-ml",
        "sub_category_id": 175,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "MLOps Platforms and Lifecycle",
            "id": 43,
            "rationale": "End-to-end managed platforms used to train, deploy, register, and govern models across their lifecycle. This is the operational control plane for production ML workflows.",
            "slug": "mlops-platforms-and-lifecycle",
            "source": "db"
          },
          "input_skill": "Azure Machine Learning",
          "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": "Azure Machine Learning",
      "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": [
        {
          "alias_text": "Unit Testing",
          "alias_type": "CANONICAL",
          "id": 865,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 8,
        "display_name": "Unit Testing",
        "id": 517,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "METHODOLOGY",
        "slug": "unit-testing",
        "sub_category_id": 44,
        "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": "Unit Testing",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Unit Testing",
      "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": "Endpoint Security",
      "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": "endpoint-security",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Log Collection",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Monitoring 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": "log-collection",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Vulnerability Scanning",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Security 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": "vulnerability-scanning",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Compliance Scanning",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Security 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": "compliance-scanning",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Endpoint Security",
    "Log Collection",
    "Vulnerability Scanning",
    "Compliance Scanning"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "Cloud Architect",
    "id": 9,
    "rationale": "Exact alias hit on cloud-engineer (1.0) \u2014 no other alias at this confidence; skill_top absent does not contradict",
    "role_archetype": null,
    "slug": "cloud-architect",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Azure Machine Learning",
      "tag": "in_db"
    },
    {
      "skill": "Unit Testing",
      "tag": "in_db"
    },
    {
      "skill": "Endpoint Security",
      "tag": "new"
    },
    {
      "skill": "Log Collection",
      "tag": "new"
    },
    {
      "skill": "Vulnerability Scanning",
      "tag": "new"
    },
    {
      "skill": "Compliance Scanning",
      "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": 9,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "MLOps Platforms and Lifecycle",
          "id": 43,
          "rationale": "End-to-end managed platforms used to train, deploy, register, and govern models across their lifecycle. This is the operational control plane for production ML workflows.",
          "slug": "mlops-platforms-and-lifecycle",
          "source": "db"
        },
        "dimension_id": 43,
        "input_skill": "Azure Machine Learning",
        "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": "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": false,
        "skill_id": null,
        "skill_tag": "new",
        "skipped_reason": "skill_not_in_db_v3_proposed"
      },
      {
        "chosen_role_id": 9,
        "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": "Unit Testing",
        "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": 517,
        "skill_tag": "in_db",
        "skipped_reason": null
      }
    ],
    "new_skills_created": 0,
    "role_dimension_saved": 0,
    "skill_dimension_saved": 0,
    "skipped": 1
  },
  "planner_output": null,
  "run_id": "47f7a4fb-2dd3-48e9-9f81-30c213b8adb5"
}

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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