Bengaluru, India
Location: Bangalore
Experience: 5+ Years
Primary Skills: MLOps, Azure Machine Learning, Python, Azure Ecosystem
We are looking for an experienced Azure ML / MLOps Engineer with strong hands-on expertise in building, deploying, managing, and monitoring machine learning models in production. The ideal candidate should have in-depth knowledge of Azure Machine Learning services, Python-based ML workflows, model deployment, automation, and production monitoring.
Design, build, deploy, and maintain scalable machine learning pipelines and production models using Azure Machine Learning.
Manage Azure ML workspaces, compute resources, environments, datastores, data assets, and model registries.
Develop and automate ML jobs, reusable components, pipelines, and endpoint deployments using the Azure ML Python SDK and Azure ML CLI.
Deploy models through real-time online endpoints and batch endpoints, ensuring reliability and scalability.
Build CI/CD pipelines for machine learning workloads using GitHub Actions and Git-based development practices.
Implement model monitoring, performance tracking, data/model drift detection, alerting, and production issue resolution.
Work with Azure Storage (Blob Storage / ADLS Gen2), Azure Key Vault, Azure Container Registry, Microsoft Entra ID, managed identities, Azure Monitor, and Application Insights.
Develop modular, tested, production-grade Python code and manage containerized ML applications using Docker.
Orchestrate machine learning workflows using Apache Airflow or similar workflow orchestration tools.
Support infrastructure automation using Terraform and implement feature store solutions for ML feature management and reuse.
Collaborate with data scientists, data engineers, and platform teams to operationalize ML models and improve deployment efficiency.
5+ years of relevant experience in ML Engineering / MLOps.
Strong hands-on experience with Azure Machine Learning and production model ownership.
Expertise in Azure ML Python SDK, Azure ML CLI, jobs, components, pipelines, and YAML-based configurations.
Advanced Python programming, including modular development, testing, packaging, and shared repository practices.
Experience with model deployment, CI/CD, Git, Docker, model monitoring, and drift detection.
Working knowledge of Azure cloud services and security integrations.
Experience with workflow orchestration tools, preferably Apache Airflow.
Practical experience implementing or managing feature stores.
Infrastructure as Code (IaC) experience using Terraform.
Experience with automated model retraining, model lifecycle management, and production alerting.
Experience improving ML platform reliability, scalability, and deployment automation.