Lead DevOps Engineer AI ML Ops

Job Location: Chennai, India

Job Title: Lead DevOps Engineer (AI Ops / ML Ops)

Work Mode: Onsite

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 guidance from seasoned 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.
  
  

• 5 to 8 Years of professional experience in in DevOps, MLOps, or systems engineering environment.
• Bachelors degree in Computer Science, Engineering, Information Technology, or a closely related technical field. Trimbles Professional ladder typically requires four or more years of formal education.
• Expertise in working with Microsoft Azure and its services including ML/AI (Azure ML, Azure DevOps, etc.) - Must Have
• Highly proficient in Python or other scripting languages (Shell / Bash / PowerShell / Perl) for automation scripting and system integration (Must have)
• Strong experience of containerization technologies (Docker) and orchestration concepts (Kubernetes).
• Strong experience of DevOps principles and practices, with understanding of CI/CD concepts and system administration.
• Strong experience with CI/CD tools such as Jenkins, GitHub Actions, or ArgoCD.
• Experience with monitoring and observability tools (Prometheus, Grafana, Datadog, New Relic).
• Hands-on with version control systems (Git) and collaborative development workflows.
• Experience with data engineering concepts and technologies (SQL, NoSQL, ETL pipelines).
• Experience with MLOps tools and frameworks (Kubeflow, MLflow, Weights & Biases, or similar).
  
  

• Experience with other cloud platforms (GCP, AWS) is a plus.
• Hands-on experience in machine learning concepts and the ML model lifecycle from development to production.
• Experience of AIOps and incident management platforms like Moogsoft, BigPanda, PagerDuty, or Opsgenie.
• Working knowledge with model serving frameworks (TensorFlow Serving, TorchServe, ONNX Runtime).
• Working knowledge of security best practices for ML systems and data governance.