Machine Learning,Azure,Python

1. Pipeline Architecture & Automation

• 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.

2. Deployment & Orchestration

• 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.

3. Optimization & Governance

• 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.

4. Collaboration

• 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.

• Required Qualifications

• Education: Bachelor’s degree in engineering, Computer Science, or a related field.

• Experience: 5+ years of total experience with a deep focus on the Azure MLOps tool stack .

• Production Mastery: Proven track record of deploying and maintaining ML models in high-scale production environments.

• Technical Proficiency: * Hands-on expertise with Azure Machine Learning and Databricks .

• Strong understanding of Kubernetes (AKS) or API-based deployment platforms.

• Solid grasp of DevOps practices and containerization (Docker).

• Experience with code quality automation tools like SonarQube .

• Soft Skills: Exceptional problem-solving skills and the ability to thrive in a fast-paced, collaborative environment.

• Desired Qualifications

• Architectural Mindset: Familiarity with broader solution architecture principles is a strong plus.

• Certifications: Azure certifications such as AI-900 , DP-100 , or AZ-305 are highly preferred.