- 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.

- Solid 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

📌 Senior ML Ops Engineer (Bengaluru)
🏢 Tata Consultancy Services
📍 Bengaluru