Senior MLOps Engineer

Summary

Join Method, a global design and engineering consultancy, as a hands-on MLOps Engineer. You will build and scale an MLOps platform for end-to-end machine learning, automating workflows, streamlining CI/CD pipelines, and operationalizing models in production. Collaborate with ML Engineers to support real-time decision-making and AI-powered services. A key role involves productionizing LLM use cases and ensuring infrastructure observability, governance, and reliability. Up to 15% travel for team and client meetings is required. Method offers competitive perks, including continuing education, flexible PTO and work-from-home policies, private medical care, a cafeteria system, group life insurance, and other location-specific benefits.

Requirements

• 5+ years of experience in MLOps engineering or software development, with a strong focus on designing, deploying, and maintaining scalable, reliable, and automated machine learning pipelines and infrastructure
• Experience deploying and managing ML services on Kubernetes using Docker
• Ability to design, manage, and debug Docker containers for ML workloads and services
• Hands-on experience with Argo Workflows (or equivalent) for orchestrating multi-step ML pipelines
• GitOps experience for pipeline automation and deployment
• Experience or working knowledge of hosting and managing on-prem LLMs (e.g., running inference, local deployment) and understanding of LLMOps

Responsibilities

• Work side-by-side with ML Engineers to understand data requirements for ML workflows, iterating on ML models based on business & technical requirements
• Develop and maintain ML models within a platform
• Implement and manage workflow orchestration
• Develop CI/CD pipeline for ML systems integrating with version control systems like Bitbucket
• Deploy and monitor API endpoints for model inference
• Implement monitoring and observability tooling for real time tracking of model performance, drift and system health
• Ensure compliance with organisational security, governance and auditability standards for all MLOps components
• Work collaboratively across cross-functional workstreams (e.g., AI engineering, data governance, DevOps, and product teams) to align on requirements, share infrastructure components, and ensure seamless end-to-end ML lifecycle integration

Benefits

• Continuing education opportunities
• Flexible PTO and work-from-home policies
• Private medical care (can be extended to your family)
• Cafeteria system as part of the Benefit platform
• Group life insurance