Senior ML Ops Engineer

Tata Consultancy Services; Dadri, Gurugram District, Bengaluru; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

Tata Consultancy Services is seeking an experienced Backend/MLOps Engineer to design, develop, and maintain scalable backend systems and ML pipelines in production. The role emphasizes Python, cloud-native architectures, and strong CI/CD practices to deliver reliable services.

The ideal candidate has 8+ years in backend/MLops, hands-on ML frameworks, and extensive experience with AWS, Docker, and Kubernetes.

Qualifications

• 8+ years of backend/MLops or DevOps experience.
• Strong Python, REST APIs, AWS, Docker and Kubernetes skills.
• Hands-on with ML frameworks (PyTorch, TensorFlow, Scikit-learn).
• Proven experience building ML training, deployment and monitoring pipelines.
• Strong knowledge of CI/CD automation, IaC, and cloud-native architectures.
• Excellent problem-solving and communication, stakeholder management, and teamwork.

Responsibilities

• Design, develop, and maintain scalable backend systems and APIs using Python and cloud-native tech.
• Build, deploy, and optimize ML models and pipelines in production.
• Develop CI/CD pipelines across platforms using Jenkins, GitHub Actions, and automation frameworks.
• Containerize apps and manage deployments with Docker and Kubernetes for high availability.
• Apply IaC practices with Terraform, CloudFormation, or equivalents.
• Collaborate with data scientists and product teams to integrate ML models into production.
• Design and manage cloud infrastructure on AWS and other clouds for ML workloads.
• Monitor performance, availability, and reliability with Prometheus, Grafana, and ELK Stack.
• Implement security best practices and access controls.
• Troubleshoot production issues and drive continuous improvements.
• Automate operational processes and deployment workflows using Python, Shell, Groovy.

Skills

Python

REST APIs

AWS

Docker

Kubernetes

ML frameworks

Tools

Terraform

CloudFormation

Job description

• Design, develop, and maintain scalable backend systems and APIs using Python and cloud-native technologies.
• Build, deploy, and optimize machine learning models, training pipelines, and ML-powered applications in production environments.
• Develop, maintain, and improve CI/CD pipelines across multiple platforms using tools such as Jenkins, GitHub Actions, and related automation frameworks.
• Containerize applications and manage deployments using Docker and Kubernetes to ensure high availability and scalability
• Implement Infrastructure as Code (IaC) practices using tools such as Terraform, CloudFormation, or equivalent solutions.
• Collaborate with data scientists, software engineers, and product teams to integrate ML models into production systems.
• Design and manage cloud infrastructure on AWS and other cloud platforms to support ML workloads and backend services. [
• Monitor application performance, availability, and reliability using tools such as Prometheus, Grafana, and ELK Stack.
• Implement security best practices, access controls, and compliance standards across applications and infrastructure.
• Troubleshoot complex production issues, perform root cause analysis, and drive continuous improvements.
• Conduct system testing for performance, scalability, security, and availability requirements.
• Automate operational processes, deployment workflows, and infrastructure management using Python, Shell, and Groovy scripting.
• Work closely with development teams to establish and enhance deployment strategies and release management processes.
• Maintain documentation for architecture, deployment procedures, monitoring, and operational practices.
• Participate in code reviews, architectural discussions, and technical decision-making.
• Mentor junior engineers and contribute to engineering excellence and best practices.
• Independently drive projects and collaborate effectively with cross-functional teams to deliver business objective.

Preferred candidate profile

• 8+ years of experience in Backend Engineering, MLOps, DevOps, or Cloud Engineering.
• Strong expertise in Python, REST APIs, AWS, Docker, and Kubernetes.
• Hands-on experience with ML frameworks such as PyTorch, TensorFlow, and Scikit-learn.
• Proven experience building ML training, deployment, and monitoring pipelines.
• Strong knowledge of CI/CD automation, Infrastructure as Code, and cloud-native architectures.
• Excellent problem-solving, communication, stakeholder management, and teamwork skill