DevOps Engineer - (AI / ML Ops)

Job Location: Chennai, India

Job Title: DevOps Engineer I / II (AI Ops / ML Ops)

Work Mode: Onsite

Recruitment Drive(Invite Only): Sep 11 & Sep 12, 2025

(https://forms.gle/ovmqLvDGnEVEn6QR8).

What You Will Do

This role offers an exciting opportunity to work in AI/ML Development and Operations (DevOps) engineering, working within a dynamic team that values reliability and continuous improvement. The successful candidate will contribute to the deployment and maintenance of AI/ML systems in production, gaining hands-on experience with MLOps best practices and infrastructure automation. This position provides a structured environment for developing core competencies in ML system operations, DevOps practices, and production ML monitoring, with direct guidance from experienced professionals.

• Assist in the deployment and maintenance of machine learning models in production environments under direct supervision, learning containerization technologies like Docker and Kubernetes.
• Support CI/CD pipeline development for ML workflows, including model versioning, automated testing, and deployment processes using tools like Azure DevOps.
• Monitor ML model performance, data drift, and system health in production environments, implementing basic alerting and logging solutions.
• Contribute to infrastructure automation and configuration management for ML systems, learning Infrastructure as Code (IaC) practices with tools like Terraform or CloudFormation.
• Collaborate with ML engineers and data scientists to operationalize models, ensuring scalability, reliability, and adherence to established MLOps procedures and best practices.

What Skills & Experience You Should Bring(Required):

• 2 to 5 years of professional experience in in DevOps, MLOps, or systems engineering environment.
• Bachelor's degree in Computer Science, Engineering, Information Technology, or a closely related technical field.
• Experience with

  Microsoft Azure

  and its services including ML/AI (Azure ML, Azure DevOps, etc.) -

  Must Have

• Foundational knowledge of DevOps principles and practices, with understanding of CI/CD concepts and basic system administration.
• Proficiency with

  Python

  or other scripting languages (Shell / Bash / PowerShell / Perl) for automation scripting and system integration -

  Must Have

• Understanding of containerization technologies (Docker) and basic orchestration concepts (Kubernetes fundamentals) -

  Must Have

• Familiarity with version control systems (Git) and collaborative development workflows.
• Basic understanding of machine learning concepts and the ML model lifecycle from development to production.

Preferred:

• Familiarity with MLOps tools and frameworks (MLflow, Kubeflow, DVC, or similar).
• Basic experience with monitoring and observability tools (Prometheus, Grafana, ELK stack).
• Understanding of Infrastructure as Code (IaC) tools like Terraform or Ansible.
• Experience with Windows/Linux system administration and command-line tools.
• Knowledge of database systems and data pipeline technologies.
• Exposure to model serving frameworks (TensorFlow Serving, TorchServe, ONNX Runtime).
• Basic understanding of security best practices for ML systems and data governance.