AI Engineer Lead- (MLops)

About the role

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.

What you'll do

• 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

What you need

• 7–11 years in data science & ai
• MLOps
• Machine learning
• SQL
• Python
• LLMs
• Databricks
• B.Tech or B.E. or M.Tech or MCA or M.Sc

Good to have

• Airflow
• Spark
• CI/CD
• FastAPI
• Azure
• Jenkins