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Mid-level MLOps role in metro Pune with moderate brand and common mid-level experience bracket.
Strong GCP/Vertex and MLOps focus moderately limits cross-industry transferability.
Explicit 5–8 years and mandatory GCP/Vertex/Terraform MLOps requirements enforce strict screening.
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Own design, build, and maintenance of scalable MLOps frameworks on Google Cloud Platform for deployment and lifecycle management of Python-based ML models.
Develop and automate CI/CD pipelines, infrastructure as code, and deployment templates ensuring production ML services are reliable, scalable, and secure.
Manage monitoring, observability, model governance, and troubleshooting for ML deployment environments including Vertex AI and related GCP services.
5 - 8 years of experience in Cloud Engineering, MLOps, or ML Platform Engineering.
Bachelor's degree in Computer Science, Engineering, Information Technology, or related discipline.
Proven hands-on experience with Google Cloud Platform (GCP) including Vertex AI and production-grade ML deployment.
Strong skills in Python-based ML deployment, CI/CD tooling (e.g., Cloud Build, Jenkins), and Infrastructure-as-Code using Terraform or similar tools.
Experienced in building automated, repeatable deployment pipelines and managing lifecycle of ML models in production on GCP.
Skilled in operationalizing ML workloads including monitoring, alerting, and troubleshooting in enterprise environments.
Comfortable leading engineering efforts on cloud-native ML infrastructure and implementing governance and compliance for ML services.