ML Data Engineer (CE50SF RM 4207)

Position: ML Data Engineer (CE50SF RM 4207)

Shift timing : 02 PM to 10 PM
Work Mode : Work From Office
Required Industry Experience : 5+ years of total development experience
Relevant Experience required : 2.5+ years of relevant Gen AI experience
Education Required: Bachelor’s / Masters / PhD: Bachelor’s degree in engineering

Must have skills:

• Python & API Development – FastAPI/Flask, REST API design, async, Pydantic, request validation
• SQL & Spark/PySpark – PySpark DataFrames, Spark SQL, UDFs, performance tuning, partitioning
• Databricks & Lakehouse – Notebooks, Jobs, Workflows, Delta Lake, Feature Store, Unity Catalog, schema evolution, cluster optimization
• Feature & Data Pipelines – Reusable feature engineering pipelines, batch scoring pipelines, data modeling (star/snowflake, SCDs), training/serving parity, point-in-time correctness
• ML Model Deployment & Serving – Real-time + batch inference, MLflow, Databricks Model Serving / Azure ML endpoints, model versioning, CI/CD for ML
• Azure Cloud, Orchestration & Monitoring – Azure Functions, ADLS, Key Vault, Event Hub; orchestration (Workflows/ADF/Airflow); Application Insights, logging, alerting; Git & Agile practices

Good to have skills:

• MLOps & Observability – MLflow, model registry, drift monitoring

Any special or skills related notes

• Hands-on and accountable for delivering working, supportable solutions
• Clear communicator who can translate between business needs and technical implementation
• Quality-focused (testing, monitoring, documentation) with attention to reliability and maintainability
• Calm under pressure when responding to incidents and prioritizing production work

Role focus: Data pipelines + APIs + product ionization
Key responsibilities

• Build:
  o ML feature pipelines
  o Batch scoring pipelines
  o Model inference endpoints
  o APIs for scoring (real-time)
• Implement model deployment (batch + real-time scoring)
• Integrate with upstream / downstream systems

Required skills

• Strong SQL + Spark / Databricks
• Experience with data modeling and feature engineering pipelines
• Python (FastAPI/Flask/REST APIs)
• API security + scaling
• Experience deploying ML models as services

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