- Design, develop, and maintain automated pipelines for input data ingestion and output model deployment to SAT environments.
- Collaborate with data engineering and AI/ML teams to ensure seamless integration of data and models across environments.
- Build CI/CD workflows for ML model lifecycle management using Azure Services.
- Ensure traceability, versioning, and reproducibility of datasets and models.
- Monitor pipeline performance, implement logging and alerting, and troubleshoot issues proactively.
- Maintain compliance with data governance, security, and operational standards.
- Document pipeline architecture, workflows, and operational procedures.
- Robust hands-on experience with Azure services




- Proficiency in Terraform for infrastructure provisioning and automation.
- Experience with containerization (Docker) and orchestration (ECS, EKS).
- Solid understanding of CI/CD practices for ML workflows.
- Proficient in Python and scripting for automation and integration tasks.
- Familiarity with SAT processes and model validation workflows.
- Experience with Generative AI model deployment and lifecycle.
- Knowledge of ML metadata tracking tools (
- Exposure to data versioning tools (e.g., DVC).
- Familiarity with security and compliance frameworks in cloud environments.

📌 MLOPs Engineer (Pune)
🏢 Tech Mahindra
📍 Pune