Sr. MLOps Engineer

We are looking for a Senior MLOps Engineer to strengthen our machine learning infrastructure in King of Prussia, Pennsylvania. This hybrid position will focus on building dependable systems that support the full model lifecycle, from data preparation and training to deployment and ongoing performance oversight.

The role works closely with machine learning and engineering partners to improve reliability, scalability, and cost efficiency across production ML operations.Responsibilities:• Lead the design, maintenance, and enhancement of the machine learning platform that supports experimentation, training workflows, model versioning, deployment, and production observability.• Develop and operate resilient data pipelines for model training and live inference, including processes for replaying historical data, recovering from source interruptions, and handling delayed or inconsistent inputs.• Establish data handling practices that preserve time-accurate training sets so models are built on information available at the correct point in time.• Create repeatable pathways for promoting validated models into production while maintaining consistency, traceability, and operational stability.• Implement standards that make training runs reproducible, allowing teams to reliably compare results and investigate performance changes.• Track model behavior in production over time, including delayed outcome validation, and surface issues that affect model quality or business impact.• Optimize infrastructure and workflows to manage compute and data costs as usage expands across larger datasets and multiple deployed models.• Collaborate with ML engineers on model assessment, production readiness, and technical decision-making related to deployment strategies and platform improvements.• Contribute to architecture planning and recommend enhancements to the broader ML and operational ecosystem using sound engineering practices.• Support cloud-native operations using AWS, Kubernetes, Terraform, and Ansible to maintain scalable and well-managed environments.