ML Ops Engineer Resume Examples & Tips for 2025

We are seeking a skilled MLOps Engineer to bridge the gap between machine learning development and production deployment, focusing on building and maintaining scalable ML infrastructure, automating ML pipelines, and ensuring smooth model deployment and monitoring. The ideal candidate will have extensive experience with cloud platforms (AWS/Azure/GCP), containerization (Docker, Kubernetes), CI/CD pipelines, and ML frameworks (TensorFlow, PyTorch), along with strong programming skills in Python and expertise in version control and monitoring tools. This critical role helps organizations maximize the business value of their ML initiatives by ensuring reliable model deployment, maintaining production quality, and establishing best practices for the ML lifecycle while collaborating with data scientists and software engineers to accelerate the delivery of ML solutions.