MLOps Engineer / Developer

MLOps Engineer / Developer

Location

ITPL, Bengaluru

work mode

4 days work from office

Interview

AMAT interview will face to face - need local candidates

Experience Required

4+ years

Salary

24 LPA

Open New Demand # AMDIA00246 - AI Engineer + ML ops - 2

JD

MLOps Engineer / Developer

Snapshot

Experience

4–6 years in ML/AI engineering or DevOps

Reports To

MLOps Technical Lead / Manager, AIML

Education

B.E./B.Tech in CS, Software Engineering, or Data Science

About the Role

Build and operate the MLOps pipelines that take AI/ML and GenAI models from experimentation to production — packaging, CI/CD delivery, model serving, and monitoring. A hands-on engineering role bridging data science and enterprise deployment.

Key Responsibilities

• Build end-to-end pipelines — data ingestion, training, evaluation, packaging, versioning, and deployment.
• Develop and maintain Jenkins CI/CD for Dev → QA → Production promotion with multi-stage gates.
• Deploy model-serving APIs on AKS using FastAPI and vLLM; apply ONNX/TensorRT optimizations.
• Set up observability — drift detection (Evidently AI), Prometheus/Grafana, Azure Monitor.
• Apply DevSecOps practices — Key Vault, Managed Identity, SonarQube, Trivy/Snyk.
• Application Development – REST, WebSocket Frameworks using FastAPI

Must-Have Skills

• 4+ years in ML/AI engineering or DevOps with hands-on production MLOps pipeline experience.

CI/CD tooling

CI tooling (UV, Ruff, Pyrefly), Jenkins (strong), Azure DevOps, GitOps concepts; Git and pre-commit workflows.

• Databricks ML pipelines (Delta Lake, Workflows, MLflow), Asset Bundles and PySpark for data processing.

Model serving

FastAPI, Docker, AKS; exposure to vLLM and ONNX/TensorRT optimization.

• Python (strong — FastAPI, Pydantic, async), Bash, YAML/SQL scripting.
• Cloud knowledge – Azure/AWS/GCP Storage, AI related services.

Databases and storage

PostgreSQL, Redis, ADLS Gen2.

Understanding containerization, Helm, and infrastructure automation.

Nice to Have

• Airflow, DVC, and experiment tracking (W&B / Comet ML).
• Terraform, KEDA, Azure APIM, and AAD RBAC configuration.
• LLM fine-tuning pipelines; Ray Serve or BentoML exposure.
• Groovy (Jenkinsfile).
• Manufacturing or semiconductor domain experience.