MLOps Engineer Resume Example

“MLOps engineer with 4+ years building and maintaining production ML infrastructure and deployment pipelines. Expert in MLflow, Kubeflow, and model monitoring systems with a focus on automating the ML lifecycle from experiment tracking through model serving and drift detection.”

• Built the ML platform that serves 30+ production models with automated retraining, A/B testing, and canary deployments
• Got model deployment time from 2 weeks down to 2 hours by building a standardized CI/CD pipeline with automated testing
• Set up model monitoring that tracks prediction drift, data quality, and latency across 25 production models. Alerts fire automatically
• Designed the feature store on Feast serving 1,000+ features at sub-10ms latency for real-time inference
• Built a GPU-optimized Kubernetes cluster with auto-scaling and spot instances. Cut training infrastructure costs by 50%
• Responsible for managing the ML infrastructure budget and reporting usage metrics to leadership monthly
• Worked with data scientists to containerize their models and make them production-ready. Some needed significant refactoring
• Maintained the experiment tracking system (MLflow) and helped teams adopt consistent logging practices
• Wrote Terraform modules for all ML infrastructure so environments could be reproduced across dev, staging, and production

Include these keywords in your resume to pass Applicant Tracking Systems.

• Google Professional Machine Learning Engineer
• Certified Kubernetes Administrator (CKA)