MLOPs Engineer

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. Strong 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.