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Role reports to：
Digital Solutions Manager位置：
Asia Pacific, India, Karnataka, Bangalore工作地點：
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Sr. ML Ops Engineer
Job Overview
We are seeking a Sr. MLOps Engineer with 5+ years of experience to design, automate, and manage the lifecycle of machine learning models. This role is focused on building high-performance, scalable ML infrastructure on Microsoft Azure that bridges the gap between data science and production-grade engineering. You will be responsible for creating a "Plug-and-Play" deployment framework that ensures our ML solutions are resilient, secure, and cost-optimized.
Key Responsibilities
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.
o Strong understanding of Kubernetes (AKS) or API-based deployment platforms.
o Solid grasp of DevOps practices and containerization (Docker).
o 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.
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