ML Data Engineer

Job Description

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:
• ML feature pipelines
• Batch scoring pipelines
• Model inference endpoints
• 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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