Lead MLOps+ AI Engineer

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.

Experience: 7+ years in ML/AI engineering or DevOps (including 4+ years of production MLOps)

Location: Bangalore (Whitefield) - Work from office, 5 days a week

Notice Period: Immediate joiners preferred (0-15 days)

Interview Mode: In-person interviews (Whitefield, Bangalore)

Education: Master's preferred, or Bachelor's in CS, Software Engineering or Data Science

Please note: An online assessment is mandatory as part of the selection process.

Key Responsibilities

• Design end-to-end pipelines - ingestion, training, evaluation, packaging, versioning, deployment and monitoring
• Own Jenkins CI/CD (Declarative/Scripted) for Dev to QA to 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 and dev tooling: Jenkins (expert), Azure DevOps, ArgoCD (GitOps); UV, Ruff, Pyrefly, pre-commit
• MLOps and data: Databricks end-to-end (Delta Lake, Workflows, MLflow, Unity Catalog), Airflow, DVC, PySpark
• Serving and 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 and 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