Senior machine learning engineer - support

Senior Machine Learning Engineer – MLOps

Location: Bangalore, Karnataka, India

Industry: Consumer Health / Healthcare Products

Employment Type: Full-time

Work Arrangement: Hybrid

About the Role

An established global consumer health organization is seeking a Senior Machine Learning Engineer – MLOps to design, automate, and manage the lifecycle of machine learning models.

The role focuses on building scalable, high-performance ML infrastructure on Microsoft Azure, bridging data science and production engineering. A key focus will be developing reusable, secure and cost-optimized deployment frameworks that enable ML solutions to move efficiently from development through to production.

Key Responsibilities

Pipeline Architecture & Automation

Design and manage end-to-end ML pipelines using Azure Machine Learning, Databricks and Py Spark. Build and maintain automated CI/CD pipelines using Git Hub Actions. Integrate Sonar Qube and other automated quality and security controls. Develop reusable, modular templates for different ML use cases. Improve deployment efficiency and operational scalability across the organization.

Deployment & Orchestration

Containerize and deploy machine learning models using Docker and Azure Kubernetes Service (AKS). Design and manage secure, robust APIs supporting interactions between ML models and downstream applications. Contribute to broader solution architecture and ensure ML components are modular and scalable. Support high availability and seamless scaling of production ML workloads.

Optimization & Governance

Manage the machine learning model lifecycle from development through production. Monitor models for data drift and implement automated data-refresh checks. Optimize models and infrastructure to maintain performance and accuracy. Implement cost-monitoring strategies for high-compute training and deployment environments. Create detailed technical documentation covering workflows, pipeline templates and optimization approaches.

Collaboration

Act as a technical liaison between Data Science, Dev Ops and IT teams. Support smooth transitions of ML solutions across development, QA and production environments. Collaborate with cross-functional technical stakeholders to resolve complex implementation and operational challenges.

Requirements

Bachelor's degree in Engineering, Computer Science or a related field. 5+ years of professional experience, with significant experience in Azure-based MLOps. Proven experience deploying and maintaining machine learning models in high-scale production environments. Hands-on expertise with Azure Machine Learning and Databricks. Strong understanding of Kubernetes / AKS or API-based deployment platforms. Experience with Dev Ops practices and Docker/containerization. Experience with code-quality automation tools such as Sonar Qube. Strong problem-solving and analytical capabilities. Ability to work effectively in a fast-paced, collaborative environment.

Preferred Qualifications

Understanding of broader solution architecture principles. Azure certifications such as AI-900, DP-100 or AZ-305. Experience designing reusable ML deployment frameworks and scalable cloud-based ML infrastructure. Strong understanding of production ML governance, monitoring and cost optimization.