Senior Platform/MLOps Engineer

Role Overview

Senior Platform/MLOps Engineer (United States) responsible for building and scaling MLOps and platform infrastructure for AI-driven manufacturing systems. The work focuses on reliable end-to-end movement of models from training to production, supporting computer vision, deep learning, and robotics workloads in factory environments.

Responsibilities

• Build and evolve production infrastructure for scalable ML and platform systems with a focus on reliability, performance, and developer productivity.
• Design and maintain end-to-end MLOps pipelines (training, deployment, and monitoring) for computer vision and robotics models.
• Design, implement, and maintain scalable ML/AI infrastructure, including training pipelines, deployment systems, and inference services.
• Build and optimize GPU-enabled workloads on Kubernetes.
• Develop CI/CD and GitOps workflows to support continuous delivery of ML and platform services.
• Collaborate with cross-functional teams to define architecture, evaluate tradeoffs, and prototype platform capabilities.
• Improve reliability using observability tooling, incident response practices, and performance optimization.
• Partner with applied AI and robotics teams to ensure infrastructure meets real-world production needs.
• Produce documentation and contribute to engineering best practices.

Requirements

• 5+ years of experience in Platform Engineering, DevOps, or Site Reliability Engineering.
• Strong programming skills in Python, Go, JavaScript, C#, or similar.
• Proven experience designing and operating MLOps pipelines in production.
• Deep knowledge of Kubernetes (including CNCF ecosystem; managed and self-hosted).
• Hands-on experience running and optimizing GPU workloads in Kubernetes clusters.
• Experience with Infrastructure as Code (e.g., Terraform) and configuration management (e.g., Ansible).
• Experience with CI/CD pipelines and GitOps delivery workflows.
• Familiarity with observability tools such as Prometheus, Grafana, and OpenTelemetry.
• Strong software engineering practices across the SDLC.
• Strong communication and cross-team collaboration.
• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.

Preferred

• Experience with highly secure or air-gapped environments.
• Mentoring engineers.
• Contributing to architectural decisions in complex distributed systems.