ML Ops developer - F2F discussion - Chennai

Tata Consultancy Services; Chennai District; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Tata Consultancy Services (TCS) is seeking an MLops developer in India to design, build, and maintain scalable MLOps pipelines for model development, deployment, and monitoring. The role requires 4-15 years of experience and collaboration with Data Scientists, Data Engineers, and Software Developers.

Interview mode: F2F. You will automate ML workflows, manage CI/CD pipelines, enable reproducibility with model versioning and experiment tracking, monitor data quality and drift, and optimize

Qualifications

• Experience in building and operating ML pipelines and CI/CD for ML applications.

Responsibilities

• Design, build, and maintain scalable MLOps pipelines for model development, deployment, and monitoring.
• Automate ML workflows including data ingestion, model training, validation, testing, and deployment.
• Collaborate with Data Scientists, Data Engineers, and Software Developers to operationalize ML models in production.
• Develop and manage CI/CD pipelines for ML applications to enable faster and reliable deployments.
• Implement model versioning, experiment tracking, and reproducibility using MLOps tools and frameworks.
• Monitor model performance, data quality, drift, and system reliability, taking corrective actions when required.
• Deploy and manage ML models using Docker and Kubernetes.
• Optimize infrastructure, cloud resources, and ML workloads for performance, scalability, and cost efficiency.
• Ensure security, governance, compliance, and best practices across the ML lifecycle.
• Research and adopt emerging MLOps tools, frameworks, and industry best practices to improve operational efficiency.

Skills

MLOps

CI/CD

Docker

Kubernetes

Experiment tracking

Model versioning

Monitoring

Cloud optimization

Tools

Docker

Kubernetes

CI/CD tooling

Job description

Greetings from Tata Consultancy Services (TCS)

What we are looking for : MLops developer

Interview Mode : F2F discussion

Experience : 4-15 Years

• Design, build, and maintain scalable MLOps pipelines for machine learning model development, deployment, and monitoring.
• Automate machine learning workflows, including data ingestion, model training, validation, testing, and deployment.
• Collaborate with Data Scientists, Data Engineers, and Software Developers to operationalize machine learning models in production environments.
• Develop and manage CI/CD pipelines for machine learning applications to enable faster and reliable deployments.
• Implement model versioning, experiment tracking, and reproducibility using MLOps tools and frameworks.
• Monitor model performance, data quality, drift, and system reliability, taking corrective actions when required.
• Deploy and manage machine learning models using containerization and orchestration technologies such as Docker and Kubernetes.
• Optimize infrastructure, cloud resources, and machine learning workloads for performance, scalability, and cost efficiency.
• Ensure security, governance, compliance, and best practices across the machine learning lifecycle.
• Research and adopt emerging MLOps tools, frameworks, and industry best practices to improve operational efficiency.