AI Engineer Lead- (MLops)
UST (company site, opens in a new tab)8+ yrsBengaluruOn-siteDirect career pageEst. 25–40 LPA
Posted 18h ago
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Job description
Structured overview of role & requirements
About This Role
- Own and manage the entire MLOps infrastructure for AI/ML and GenAI solutions, from model packaging and CI/CD to production monitoring.
- Design and maintain end-to-end ML pipelines including ingestion, training, evaluation, deployment, and monitoring with DevSecOps compliance.
- Set engineering standards across the Enterprise AI MLOps practice and mentor engineering teams.
Minimum Requirements
- 8+ years in ML/AI engineering or DevOps with at least 4+ years in production MLOps pipelines.
- Bachelor's degree required; Master's degree preferred in Computer Science, Software Engineering, or Data Science.
- Expertise in Jenkins CI/CD (Declarative and Scripted), Azure DevOps, ArgoCD, and related DevOps tools.
- Experience in deploying model-serving APIs on Azure Kubernetes Service (AKS) using technologies like FastAPI, ONNX/TensorRT.
Ideal Candidate Profile
- Proven technical leadership with experience in mentoring engineers and stakeholder communication in MLOps contexts.
- Strong hands-on experience with Databricks (Delta Lake, MLflow), Airflow, DVC, PySpark, and MLOps observability stacks (Prometheus, Grafana).
- Expertise in secure pipeline design (Key Vault, Managed Identity, SonarQube, Trivy/Snyk) and advanced deployment strategies like blue/green, canary, and shadow testing.