Key Responsibilities\n\n1. Pipeline Architecture & Automation\n\n· 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.\n\n· DevSecOps Integration: Build and maintain automated CI/CD pipelines using GitHub Actions, integrating SonarQube to enforce strict code quality and security standards.\n\n· Reusable Frameworks: Develop modular templates for various ML use cases to streamline deployment and drive operational efficiency across the enterprise.\n\n2. Deployment & Orchestration\n\n· Containerization: Utilize Azure Kubernetes Service (AKS) and Docker to containerize and deploy ML models, ensuring high availability and seamless scaling.\n\n· API Management: Design and manage robust, secure APIs to facilitate seamless interactions between ML models and downstream applications.\n\n· Solution Architecture: Understand and contribute to the overall system architecture to ensure ML components are modular and scalable.\n\n3. Optimization & Governance\n\n· Model Lifecycle Management: Perform model optimization, monitor for data drift, and implement automated data refresh checks to maintain model accuracy.\n\n· Cost Engineering: Implement cost-monitoring strategies to ensure efficient resource utilization during high-compute training and deployment phases.\n\n· Documentation: Provide detailed technical documentation for workflows, pipeline templates, and optimization strategies to ensure long-term maintainability.\n\n4. Collaboration\n\n· 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.\n\n---