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Mid-level ML role in Bangalore with popular title but Azure MLOps specialization moderates applicant density.
Azure-centric MLOps and Databricks focus moderately limit cross-industry portability of candidates' backgrounds.
Explicit 5+ years plus mandatory Azure MLOps, AKS, Databricks, and CI/CD tool requirements increase shortlisting strictness.
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Design and automate scalable, end-to-end ML pipelines using Azure ML, Databricks, and PySpark for large-scale data processing and model training.
Build and maintain automated CI/CD pipelines integrating DevSecOps practices, containerize ML models using AKS and Docker, and manage secure API deployment.
Optimize model lifecycle management including monitoring for data drift and cost engineering; collaborate cross-functionally to ensure smooth transitions from development to production environments.
Bachelor’s degree in Engineering, Computer Science, or related field.
Minimum 5+ years experience with a strong focus on Azure MLOps tool stack and production deployment of ML models.
Hands-on expertise with Azure Machine Learning, Databricks, Kubernetes (AKS), Docker, and CI/CD tools like GitHub Actions and SonarQube.
Work Location: Hybrid in Bangalore, India.
Experienced ML Ops engineer with proven ability to deploy and maintain high-scale production ML models on Azure platforms.
Strong technical proficiency in containerization, pipeline automation, and DevSecOps integration within a cloud environment.
Operates at the intersection of data science and engineering, able to architect and maintain modular, scalable ML infrastructure and frameworks.