Posted:3 weeks ago|
Platform:
On-site
Full Time
Key Responsibilities:
. Build and maintain ML pipelines for training, validation, deployment, and monitoring. Implement CI/CD for ML artifacts, including data versioning and model registries. Monitor model performance and drift trigger retraining workflows as needed. Manage infrastructure (cloud, containerized, on-prem) for high-availability AI services. Collaborate with data, ML, and ops teams to deliver frictionless MLOps lifecycleRequired Skills:. Strong knowledge of MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI). Proficiency in Python, Bash, Docker, and Kubernetes. Familiarity with cloud infra (AWS/GCP/Azure) and IaC (Terraform, CloudFormation). Experience in model monitoring, logging, and alerting. Bonus: experience in BPS / regulated domains with compliance-aware deployment
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17.0 - 22.5 Lacs P.A.
Experience: Not specified
Salary: Not disclosed
