Sr. Machine Learning Engineer- Support

· Scalable ML Pipelines: Design and manage end-to-end ML pipelines using Azure ML , Databricks , and PySpark to handle large-scale data processing and model training.

· DevSecOps Integration: Build and maintain automated CI/CD pipelines using GitHub Actions , integrating SonarQube to enforce strict code quality and security standards.

· Reusable Frameworks: Develop modular templates for various ML use cases to streamline deployment and drive operational efficiency across the enterprise.

· Containerization: Utilize Azure Kubernetes Service (AKS) and Docker to containerize and deploy ML models, ensuring high availability and seamless scaling.

· API Management: Design and manage robust, secure APIs to facilitate seamless interactions between ML models and downstream applications.

· Solution Architecture: Understand and contribute to the overall system architecture to ensure ML components are modular and scalable.

· Model Lifecycle Management: Perform model optimization, monitor for data drift , and implement automated data refresh checks to maintain model accuracy.

· Cost Engineering: Implement cost-monitoring strategies to ensure efficient resource utilization during high-compute training and deployment phases.

· Documentation: Provide detailed technical documentation for workflows, pipeline templates, and optimization strategies to ensure long-term maintainability.

· Cross-Functional Synergy: Act as the technical liaison between Data Scientists, DevOps, and IT teams to ensure smooth model transitions across Dev, QA, and Production environments.