MLOps Engineer

Project Overview

Responsibilities

• Design and maintain MLOps solutions for production ML and Generative AI systems.
• Build ML pipelines for training, testing, deployment, monitoring, and retraining.
• Manage model versioning, promotion, deployment, rollback, and retirement.
• Build scalable ML infrastructure for training and inference, including GPU workloads on Kubernetes.
• Develop CI/CD pipelines with automated testing and deployment.
• Implement model testing, data validation, monitoring, and drift detection.
• Ensure production ML reliability through monitoring, alerting, incident response, and troubleshooting.
• Create reusable MLOps templates, pipeline components, standards, and documentation.
• Collaborate with Platform, Security, and Architecture teams on reliable and secure AI infrastructure.
• Build and support production GenAI and LLM solutions with routing, prompt management, caching, rate limiting, and guardrails.
• Implement LLM and agentic AI monitoring and security, including data protection and usage tracking.
• Optimize infrastructure and costs, evaluate technologies, contribute to architecture, mentor colleagues, and share knowledge.