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.
Positive 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).

📌 Machine Learning Operations (Bengaluru)
🏢 MyCareernet
📍 Bengaluru