ML Ops Developer

Tata Consultancy Services; Chennai District; On-site; INR 1,200,000 - 2,500,000; Full time

Job summary

Tata Consultancy Services in Chennai seeks an experienced ML Ops Developer to design, deploy, and monitor scalable machine learning solutions.

You will build end-to-end MLOps pipelines, implement CI/CD and Continuous Training, and manage infrastructure with Docker, Kubernetes, MLflow, and Kubeflow. Collaborate with data scientists and engineers to ensure secure, compliant deployments on AWS, Azure, or GCP.

Qualifications

• Hands-on experience in MLOps frameworks, cloud platforms, CI/CD pipelines, containerization, model monitoring, and automation of the end-to-end ML lifecycle.

Responsibilities

• Design, develop, and maintain end-to-end MLOps pipelines across the ML lifecycle, including data preparation, model training, validation, deployment, monitoring, and retraining.
• Implement CI/CD and Continuous Training 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 operationalise Machine Learning solutions.
• Drive best practices for version control, model lifecycle management, infrastructure automation, scalability and reliability.

Skills

MLOps

Docker

Kubernetes

MLflow

Kubeflow

CI/CD

Cloud platforms

Model monitoring

Automation

Tools

Docker

Kubernetes

MLflow

Kubeflow

CI/CD

AWS

Azure

GCP

Job description

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 operationalise Machine Learning solutions.
• Drive best practices for version control, model lifecycle management, infrastructure automation, scalability and reliability.