MLOps Technology Lead

MLOps Technology Lead

Experience: 5–11 years

Job Locations: Bengaluru, Hyderabad, Chennai, Pune, Thiruvananthapuram, Mysuru, Bhubaneswar, Chandigarh, Hubballi, Jaipur

Position Overview

We are seeking a seasoned MLOps Technology Lead to architect, develop, and oversee scalable machine learning platforms and production-grade ML pipelines. This role requires close collaboration with data scientists, software engineers, and product teams to optimize the entire machine learning lifecycle, from model creation and deployment to continuous monitoring and performance tuning.

The ideal candidate will possess extensive hands-on expertise with Docker, Kubernetes, CI/CD workflows, ML model deployment, and production operations, along with the capability to lead technical projects and mentor engineering teams effectively.

Main Responsibilities

• Drive comprehensive MLOps initiatives, ensuring seamless operationalization of machine learning models from development to live environments.
• Design and manage containerized deployment environments using Docker to ensure consistency and reproducibility across environments.
• Develop, deploy, and fine-tune Kubernetes-based workloads for ML services, batch processing tasks, and platform components.
• Create and maintain CI/CD pipelines for ML workflows, including version control for code, data, and model artifacts.
• Implement robust monitoring and alerting systems to track model performance, detect data drift, and ensure infrastructure health.
• Address production issues through troubleshooting, root cause analysis, and system reliability enhancements.
• Define platform standards, establish security controls, access policies, and operational procedures for scalable and secure deployment.
• Collaborate with data science teams to optimize model training, deployment, serving, and inference processes.
• Lead architecture reviews, provide mentorship to engineering staff, and advocate for best practices in MLOps across teams.

Essential Skills and Qualifications

• Practical experience in MLOps and deploying machine learning models in production environments.
• Deep knowledge of Docker and Kubernetes container orchestration.
• Proven experience designing and implementing CI/CD pipelines and automating ML workflows.
• Understanding of model lifecycle management, including model registries and version control.
• Experience with model serving techniques, online inference, and batch inference methodologies.
• Familiarity with monitoring tools, model performance metrics, data drift detection, and incident response management.
• Strong troubleshooting capabilities, technical leadership qualities, and effective stakeholder communication skills.

Educational Background

B.Tech/B.E., BCA, B.Sc., M.Tech, MCA, or M.Sc.

Desired Experience

Proven experience in building scalable ML platforms, standardizing deployment workflows, enhancing production reliability, and mentoring engineering teams within hybrid work environments.