Lead MLOps Engineer

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Role Overview: We are looking for an experienced Lead MLOps Engineer to architect, develop, and implement robust cloud-based MLOps solutions on Microsoft Azure. In this role, you will lead the operations team and oversee both DevOps and MLOps pipelines, ensuring seamless integration and delivery of machine learning solutions. The ideal candidate will collaborate with application and data development teams, data scientists, AI/ML engineers and other stakeholders to deliver scalable and efficient machine learning pipelines.

Key Responsibilities

· Lead the operations team, providing technical guidance and mentorship.

· Oversee and manage both DevOps and MLOps pipelines, ensuring best practices and operational excellence.

· Design, implement, and manage scalable ML pipelines using Azure ML, Databricks, and PySpark.

· Build and maintain automated CI/CD pipelines with GitHub and GitHub Actions, integrating SonarQube for code quality and security.

· Containerize and deploy ML models using Azure Kubernetes Service (AKS) to ensure high availability and scalability.

· Develop reusable templates for various ML use cases to streamline deployment and improve operational efficiency.

· Design and manage APIs for seamless integration between ML models and applications, ensuring robust, secure, and scalable interfaces.

· Optimize models, monitor data drift, perform data refresh checks, and ensure cost-efficient ML pipelines.

· Implement cost monitoring and management strategies for model training and deployment.

· Collaborate with data scientists, DevOps, and IT teams to deploy and manage ML models across environments.

· Provide comprehensive documentation for ML workflows, pipeline templates, and optimization strategies to support cross-team collaboration.

· Understand overall architecture and contribute to scalable solution design.

Required Qualifications:

· Bachelor’s degree in engineering, computer science, or a related field.

· 6+ years of total work experience, with at least 2–3 years in Azure MLOps.

· Strong knowledge of solution architecture and/or machine learning with a focus on MLOps.

· Hands-on experience deploying and maintaining ML models in production.

· Solid understanding of DevOps practices in cloud environments.

· Experience with containerization (Docker) and orchestration (Kubernetes).

· Excellent problem-solving skills and ability to work collaboratively in a fast-paced environment.

· Experience deploying MLOps solutions on AKS or API platforms.

· Proficiency with Azure Machine Learning and Databricks.

· Experience with code quality automation tools such as SonarQube.

Desired Qualifications:

· Familiarity with solution architecture is a plus.

· Azure certifications such as AI-900, DP-100, or AZ-305 are preferred.

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