Machine Learning Operations Engineer (MLOps)

SAP•Bellevue, WA

5d•$101,900 - $224,000•Hybrid

About The Position

We help the world run betterAt SAP, we keep it simple: you bring your best to us, and we'll bring out the best in you. We're builders touching over 20 industries and 80% of global commerce, and we need your unique talents to help shape what's next. The work is challenging – but it matters. You'll find a place where you can be yourself, prioritize your wellbeing, and truly belong. What's in it for you? Constant learning, skill growth, great benefits, and a team that wants you to grow and succeed. As a MLOps Engineer in Bellevue, you will join a new team dedicated to building and maintaining customer support related AI and agentic solutions that collaborate with thousands of SAP support engineers worldwide and that interact with all of SAP’s customers as part of SAP’s mission critical and daily customer support offerings in preventive, reactive and value-adding scenarios across SAP’s product and support portfolio. Your team will be part of the AI & Support Engineering organization with a decade of experience delivering enterprise AI at scale.

Requirements

3+ years of experience in MLOps, DevOps or platform engineering for ML or AI systems.
Proficiency with containerization and orchestration (Docker, Kubernetes).
Experience with ML lifecycle tooling (e.g. MLflow, Kubeflow).
Hands-on knowledge of cloud platforms (Azure) and infrastructure-as-code (e.g. Terraform).
Familiarity with LLM serving, inference optimization, and model-serving frameworks.
Solid software engineering and scripting skills, particularly in Python.
Understanding of monitoring, observability, and alerting for production systems.
Experience building automated, reliable CI/CD pipelines.
Knowledge of security and compliance requirements for enterprise software.
Passion for AI with a keen sense for automation.
Fluent in English.

Responsibilities

Build and maintain AI infrastructure and pipelines.
Design CI/CD workflows for model training, evaluation, deployment, and monitoring.
Automate model versioning, experiment tracking, and reproducibility practices.
Collaborate with engineers to operationalize and serve models reliably.
Manage the AI infrastructure stack, including GPU resources, orchestration, and serving frameworks.
Optimize spend and resource utilization.
Partner with security engineers to ensure solutions meet enterprise security, compliance, and availability standards.
Establish MLOps best practices.
Report on progress to leadership.