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

Work arrangement
No