Sr. MLOps Engineer

Description
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. Requirements
At least 4 years of experience in software engineering, data engineering, or a closely related technical discipline.

Minimum 2 years of hands-on experience running machine learning systems in production environments. Demonstrated success building an ML platform from the ground up or significantly scaling an existing one while supporting its long-term evolution. Experience managing several production models with demanding training processes and operational dependencies. Strong background in building and maintaining large-scale production data pipelines, including attention to reliability and cost control.

Working knowledge of machine learning concepts and evaluation methods sufficient to engage in technical review with ML practitioners. Proficiency with Python and practical experience in Kubernetes-based environments, PostgreSQL, and infrastructure or workflow automation tools. Strong communication, ownership, analytical thinking, and cross-functional collaboration skills in complex engineering settings. Technology Doesn't Change the World, People Do.

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