MLOps Technical Lead

MLOps Technical Lead

Location

ITPL, Bengaluru

work mode

4 days work from office

Interview

AMAT interview will face to face - need local candidates

Experience Required

8+ years

Salary

28 LPA

Open New Demand # AMDIA00247 - AI Engineer + ML ops - Lead - 1

MLOps Technical Lead

Snapshot

Experience

8+ years in ML/AI engineering or DevOps (incl. 4+ yrs production MLOps)

Report To

Manager, AIML

Education

Master's (preferred) or Bachelor's in CS, Software Engineering, or Data Science

About the Role

Own the complete MLOps backbone for AI/ML and GenAI solutions — from model packaging and CI/CD delivery through production monitoring. Bridge data-science experimentation and enterprise-scale deployment and set engineering standards for the Enterprise AI MLOps practice.

Key Responsibilities

• Design end-to-end pipelines — ingestion, training, evaluation, packaging, versioning, deployment, and monitoring.
• Own Jenkins CI/CD (Declarative/Scripted) for Dev → QA → Production with multi-stage gates.
• Deploy model-serving APIs on AKS (FastAPI, vLLM); apply ONNX/TensorRT and blue/green rollouts.
• Build observability stacks — drift detection (Evidently AI), Prometheus/Grafana, Azure Monitor.
• Enforce DevSecOps — zero-credential pipelines via Key Vault + Managed Identity, SonarQube, Trivy/Snyk.
• Set MLOps engineering standards and mentor engineers across the practice.

Must-Have Skills

• 7+ years in ML/AI engineering or DevOps with 4+ years operating production MLOps pipelines.

CI/CD & Dev tooling

Jenkins (expert), Azure DevOps, ArgoCD (GitOps); UV, Ruff, Pyrefly, pre-commit.

MLOps & data

Databricks end-to-end (Delta Lake, Workflows, MLflow, Unity Catalog), Airflow, DVC, PySpark.

Serving & infra

FastAPI, ONNX/TensorRT, AKS, Helm, Docker, KEDA, Azure APIM, Terraform.

Security

Key Vault, AAD RBAC, Managed Identity; zero-credential pipeline design

Languages

Python (expert — FastAPI, Pydantic, async), Groovy (Jenkinsfile), Bash, YAML/SQL.

Databases & storage

PostgreSQL, Redis, ADLS Gen2.

• A/B testing and canary/shadow deployment experience.
• Proven technical leadership, mentoring, and stakeholder communication.

Nice to Have

• LLM fine-tuning pipelines and large-scale model-serving optimization.
• Ray Serve, BentoML for advanced model serving patterns.
• Experiment tracking and governance (W&B / Comet ML, Unity Catalog policies).
• Multi-cloud or hybrid deployment strategy and cost optimization.
• Semiconductor or manufacturing domain experience.