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Pipeline run

e3e6ec96-d2de-4c1f-b684-ce7ee6bfe51b

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description

Nature of work

—

no_db_connection

Tech stack maturity

Mainstream Modern

AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)

1.70 / 5

· Title match

✓ Has AI skill

✓ AI skill (primary)

· AI skill (secondary)

· On AI team

· Builds AI products

vocab breakdown (legacy)

Assistants (×1): —

Frameworks (×2): Vertex AI

Models / concepts (×3): MLOps, AI, ML, Machine Learning

Evidence — skills matched in JD (18)

AWS SageMaker Azure ML Studio GCP Vertex AI PySpark Azure Databricks MLFlow KubeFlow AirFlow GitHub Actions AWS CodePipeline Kubernetes AKS Terraform FastAPI Python CI/CD Bash Unix

Skill cluster (0 dimension groups, role-scoped)

No dimension groups computed for this JD.

Status: extract_from_jd_done Created: 2026-05-10T08:44:10.309141Z Updated: 2026-05-10T08:44:10.309141Z

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

No chosen role stored for this run.

Job description

MLOps Engineer
Key words -Skillset
• AWS SageMaker, Azure ML Studio, GCP Vertex AI
• PySpark, Azure Databricks
• MLFlow, KubeFlow, AirFlow, Github Actions, AWS CodePipeline
• Kubernetes, AKS, Terraform, Fast API

Responsibilities
• Model Deployment, Model Monitoring, Model Retraining
• Deployment pipeline, Inference pipeline, Monitoring pipeline, Retraining pipeline
• Drift Detection, Data Drift, Model Drift
• Experiment Tracking
• MLOps Architecture
• REST API publishing

Job Responsibilities:

- Research and implement MLOps tools, frameworks and platforms for our Data Science projects.
- Work on a backlog of activities to raise MLOps maturity in the organization.
- Proactively introduce a modern, agile and automated approach to Data Science.
- Conduct internal training and presentations about MLOps tools’ benefits and usage.

Required experience and qualifications:

· Wide experience with Kubernetes.

· Experience in operationalization of Data Science projects (MLOps) using at least one of the popular frameworks or platforms (e.g. Kubeflow, AWS Sagemaker, Google AI Platform, Azure Machine Learning, DataRobot, DKube).

· Good understanding of ML and AI concepts. Hands-on experience in ML model development.

· Proficiency in Python used both for ML and automation tasks. Good knowledge of Bash and Unix command line toolkit.

· Experience in CI/CD/CT pipelines implementation.

Skills from this JD

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

AWS SageMaker Primary No API 2 row (run stopped after API 1 or history missing)

Azure ML Studio Primary No API 2 row (run stopped after API 1 or history missing)

GCP Vertex AI Primary No API 2 row (run stopped after API 1 or history missing)

PySpark Primary No API 2 row (run stopped after API 1 or history missing)

Azure Databricks Primary No API 2 row (run stopped after API 1 or history missing)

MLFlow Primary No API 2 row (run stopped after API 1 or history missing)

KubeFlow Primary No API 2 row (run stopped after API 1 or history missing)

AirFlow Primary No API 2 row (run stopped after API 1 or history missing)

GitHub Actions Primary No API 2 row (run stopped after API 1 or history missing)

AWS CodePipeline Primary No API 2 row (run stopped after API 1 or history missing)

Kubernetes Primary No API 2 row (run stopped after API 1 or history missing)

AKS Primary No API 2 row (run stopped after API 1 or history missing)

Terraform Primary No API 2 row (run stopped after API 1 or history missing)

FastAPI Primary No API 2 row (run stopped after API 1 or history missing)

Python Primary No API 2 row (run stopped after API 1 or history missing)

Bash Secondary No API 2 row (run stopped after API 1 or history missing)

Unix Secondary No API 2 row (run stopped after API 1 or history missing)

CI/CD Primary No API 2 row (run stopped after API 1 or history missing)

Library artifacts (this run)

No artifact rows for this run.

API 1 — extract-from-jd click to toggle

{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "AWS SageMaker"
    },
    {
      "is_primary": true,
      "skill_name": "Azure ML Studio"
    },
    {
      "is_primary": true,
      "skill_name": "GCP Vertex AI"
    },
    {
      "is_primary": true,
      "skill_name": "PySpark"
    },
    {
      "is_primary": true,
      "skill_name": "Azure Databricks"
    },
    {
      "is_primary": true,
      "skill_name": "MLFlow"
    },
    {
      "is_primary": true,
      "skill_name": "KubeFlow"
    },
    {
      "is_primary": true,
      "skill_name": "AirFlow"
    },
    {
      "is_primary": true,
      "skill_name": "GitHub Actions"
    },
    {
      "is_primary": true,
      "skill_name": "AWS CodePipeline"
    },
    {
      "is_primary": true,
      "skill_name": "Kubernetes"
    },
    {
      "is_primary": true,
      "skill_name": "AKS"
    },
    {
      "is_primary": true,
      "skill_name": "Terraform"
    },
    {
      "is_primary": true,
      "skill_name": "FastAPI"
    },
    {
      "is_primary": true,
      "skill_name": "Python"
    },
    {
      "is_primary": false,
      "skill_name": "Bash"
    },
    {
      "is_primary": false,
      "skill_name": "Unix"
    },
    {
      "is_primary": true,
      "skill_name": "CI/CD"
    }
  ],
  "run_id": null
}

API 2 — extract-details

{}

API 3 — final-role-output

{}

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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