Method is a global design and engineering consultancy founded in 1999. We believe that innovation should be meaningful, beautiful and human. We craft practical, powerful digital experiences that improve lives and transform businesses. Our teams based in New York, Charlotte, Atlanta, London, Poland, Bengaluru, and remote work with a wide range of organizations in many industries, including Healthcare, Financial Services, Retail, Automotive, Aviation, and Professional Services.
Method is part of GlobalLogic, a digital product engineering company. GlobalLogic integrates experience design and complex engineering to help our clients imagine what’s possible and accelerate their transition into tomorrow’s digital businesses. GlobalLogic is a Hitachi Group Company.We’re seeking a hands-on MLOps Engineer to join our Data & AI team You will be responsible for building and scaling a MLOps platform that enables end to end machine learning. Partnering closely with ML Engineers, you will automate ML workflows, streamline CI/CD pipelines and operationalise models in production environments to support real time decision making and AI power services.
You will also play a key role in productionising LLM use cases and ensuring the overall observability, governance and reliability of the MLOps infrastructure. Travel for team and client meetings is required, typically up to 15%.
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.
Qualifications:
- 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.
Why Method?
We look for individuals who are smart, kind and brave. Curious people with a natural ability to think on their feet, learn fast, and develop points-of-view for a constantly changing world find Method an exciting place to work. Our employees are excited to collaborate with dispersed and diverse teams that bring together the best in thinking and making. We champion the ability to listen, and believe that critique and dissonance lead to better outcomes. We believe everyone has the capacity to lead and look for proactive individuals who can take and give direction, lead by example, enjoy the making as much as they do the thinking, especially at senior and leadership levels.We believe in work/life balance.
Seriously. We offer a ton of competitive perks, including:
- 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
- Creative TAX-deductible cost
- Other location specific perks (just ask!)
Next Steps
If Method sounds like the place for you, please submit an application. Also, let us know if you have a presence online with a portfolio, GitHub, Dribbble or other platform.* For information on how we process your personal data please see Privacy.
