Machine Learning Operations (MLOps) Engineer

Job description

Key Skills: MLOps, Vertex AI (Google), DevOps, GCP, Python, ML Flow, Kubeflow Roles and Responsibilities: • Design, build, and maintain scalable MLOps frameworks on GCP, including repeatable deployment processes across environments. • Automate ML model deployment and lifecycle management, including versioning, artifact handling, retraining, rollback, and release governance. • Implement CI/CD pipelines for ML applications and services, integrating source control, testing, and deployment workflows. • Apply infrastructure automation practices (IaC) to provision and manage environments reliably across development, testing, and production. • Ensure production readiness through monitoring, alerting, observability, and operational support for deployed ML workloads. Skills Required: • 5 - 8 years of experience in Cloud Engineering, MLOps, or ML Platform Engineering. • DevOps practices for production engineering and operational excellence. • Google Cloud Platform (GCP) engineering experience for cloud-native ML operations. • MLOps experience, including model lifecycle management and operationalization patterns. • Vertex AI (Google) for deploying and managing ML models in production. Good to Have: • Python for building and operationalizing ML model workflows. Education: B.E., B.Tech, B.Tech M.Tech (Dual), M. Tech, M.E., M.Sc., MCA, or MCM in Computer Application or Information Science and Technology (or related field).