DevOps Engineer - AI & Machine Learning

Overview

A wide range of DevOps Engineer roles focused on AI and Machine Learning are currently available across India in 2026. With over 1,039 active listings, these positions span established enterprises, growing technology firms and AI-focused startups. They offer opportunities to work with large-scale, distributed systems that power the development, training and deployment of AI-driven products.

Responsibilities

Professionals in these roles will help bridge the gap between software engineering, data science and IT operations to ensure that machine learning workloads remain scalable, reliable and secure. Common responsibilities include designing and maintaining CI/CD pipelines for ML models, containerising applications with tools such as Kubernetes and Docker, and provisioning infrastructure using Infrastructure as Code. Additional duties often involve optimising cloud resources for compute-intensive workloads, implementing automated testing and monitoring, and supporting the transition of research models into production-ready services.

Work Environment

These roles are distributed across India's major tech hubs, including Bengaluru, Hyderabad, Chennai, Pune, Mumbai and Delhi, with many offering hybrid and fully remote working arrangements. Teams are typically cross-functional, collaborating closely with ML engineers, platform engineers and security specialists. A strong emphasis is placed on reproducibility, cost efficiency and compliance throughout the model lifecycle.

Growth Opportunities

The demand for DevOps and MLOps expertise continues to grow in 2026, with many of these roles offering exposure to cutting-edge AI workloads, career progression into platform or ML infrastructure engineering, and the chance to help scale mission-critical products used on a national and global scale.

• CI/CD & Automation: Experience building and maintaining CI/CD pipelines, preferably for AI or data-driven applications.
• Cloud Platforms: Working knowledge of at least one major cloud provider, such as AWS, Microsoft Azure or Google Cloud Platform.
• Containers & Orchestration: Hands-on experience with Docker and Kubernetes, or equivalent container orchestration tools.
• Scripting & Programming: Proficiency in at least one scripting language, such as Python, Bash or Go.
• Infrastructure as Code: Familiarity with tools like Terraform, Pulumi or Ansible for automated provisioning.
• MLOps Awareness: An understanding of ML workflows, model versioning, feature stores or model serving is highly desirable.
• Observability & Security: Experience with monitoring, logging, alerting and secure secrets management.
• Collaboration: Strong communication skills with the ability to work across engineering, data and research teams.
• Problem-Solving: A proactive and analytical mindset with a focus on scalability, reliability and performance.