1 month ago
Bengaluru, IndiaSenior
Responsibilities
- Design and manage scalable end-to-end machine learning pipelines using Azure Machine Learning, Databricks, and PySpark.
- Build and maintain automated CI/CD pipelines with GitHub Actions and SonarQube for code quality and security.
- Develop reusable, modular deployment templates for enterprise ML use cases.
- Containerize and deploy ML models using Azure Kubernetes Service and Docker for high availability and scaling.
- Design and manage secure APIs and contribute to modular, scalable solution architecture.
- Monitor model lifecycle performance, data drift, automated data refreshes, and resource costs.
- Document workflows, pipeline templates, and optimization strategies.
- Coordinate model transitions across development, QA, and production with data science, DevOps, and IT teams.
Requirements
- Bachelor’s degree in engineering, Computer Science, or a related field.
- 5+ years of total experience with a deep focus on the Azure MLOps tool stack.
- 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 or API-based deployment platforms.
- Solid knowledge of DevOps practices and Docker-based containerization.
- Experience with SonarQube or similar code quality automation tools.
- Strong problem-solving skills and ability to work in a fast-paced, collaborative environment.
- Familiarity with broader solution architecture principles is preferred.
- Azure AI-900, DP-100, or AZ-305 certifications are highly preferred.
Benefits
- Hybrid work arrangement in Bangalore, Karnataka, India.
Tech Stack
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