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Tata Consultancy Services · Chennai, Tamil Nadu, India

ML Ops Developer

seniorfull timePosted today
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Stack mentioned

mlopsmachine-learningci/cdtest-automationdockerkubernetesmlflowkubeflowobservabilitydata-governanceawsazuregcp

ML Ops Developer

Experience

4 to 12 Years

Joining Location

Chennai

Interview Location

Chennai

Job Summary

We are seeking a highly skilled ML Ops Developer with strong expertise in building, deploying, monitoring, and managing Machine Learning solutions at scale. The ideal candidate should have hands-on experience in MLOps frameworks, cloud platforms, CI/CD pipelines, containerization, model monitoring, and automation of the end-to-end ML lifecycle.

Key Responsibilities

- Design, develop, and maintain end-to-end MLOps pipelines across the Machine Learning lifecycle, including data preparation, model training, validation, deployment, monitoring, and retraining.

- Implement CI/CD and Continuous Training (CT) pipelines for ML workflows with automated testing, model promotion, rollback strategies, and reproducible builds.

- Build and manage scalable ML infrastructure using Docker, Kubernetes, MLflow, Kubeflow, or equivalent MLOps platforms.

- Develop and support model serving and deployment frameworks for batch, real-time, and streaming workloads.

- Establish monitoring and observability solutions for ML systems, including model performance, feature drift, concept drift, data quality, and operational health.

- Configure alerting mechanisms and perform root cause analysis for production ML issues.

- Deploy and manage ML workloads on cloud platforms such as AWS, Azure, or GCP using cloud-native services.

- Implement security, governance, access controls, audit logging, and compliance standards for enterprise-grade ML platforms.

- Collaborate with Data Scientists, Data Engineers, Platform Engineers, and Business teams to operationalize Machine Learning solutions.

- Drive best practices for version control, model lifecycle management, infrastructure automation, scalability, and reliability.

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