MLOps Engineer

W2 Only. Candidates must have unrestricted, permanent authorization to work in the United States. No visa sponsorship, no visa transfers, no employment-based immigration support required now or at any point in the future. No C2C. No third parties* Must have subject matter expertise in: AWS Lambda AWS SageMaker MLOps Platform Engineer | Remote (U.S.) | Full-Time, Salaried | $140K-$155K Base Salary Location: Fully Remote (U.S.) Hours: EST This is a production-focused MLOps Platform Engineering role centered on deploying, operating, monitoring, scaling, governing, and optimizing machine learning systems in production. This is not a model development or data science position. Instead, you'll build the infrastructure, automation, and operational processes that enable ML Engineers and Data Scientists to reliably deploy, monitor, scale, govern, and operate machine learning models in production at enterprise scale , with a focus on platform maturity, production ML infrastructure, inference optimization, scalability, and operational excellence . Key Responsibilities Design, deploy, and operate enterprise-scale ML platforms across Development, QA, and Production environments Build and manage AWS SageMaker infrastructure, including Model Registry, Pipelines, Endpoints, model promotion, versioning, and deployment workflows Design AWS-based ML infrastructure utilizing AWS Lambda and other cloud-native services to support scalable machine learning workflows and production inference Design and implement reliable, scalable, and governed production ML platforms supporting the complete machine learning lifecycle Develop and maintain CI/CD pipelines for ML workloads, deployment automation, and release management Partner closely with ML Engineers, Data Scientists, DevOps, and Platform Engineering teams to operationalize machine learning systems safely and reliably Required Experience Strong hands-on AWS experience designing and operating production ML platforms using AWS SageMaker , including Model Registry, Pipelines, Endpoints, and multi-environment deployments Experience designing AWS-based ML solutions utilizing AWS Lambda for event-driven processing, automation, and production machine learning workflows Deep understanding of production MLOps architecture and the complete machine learning lifecycle from development through production Experience with model versioning, governance, approval workflows, promotion, rollback, and lifecycle management Strong CI/CD experience for ML workloads, including Jenkins or a comparable pipeline automation platform Kubernetes, Docker, containerization, infrastructure automation, and platform engineering experience Experience deploying and operating PyTorch and TensorFlow models in production Preferred Experience Transformer-based NLP models, semantic search, vector search, ranking/reranking systems, and enterprise AI platforms Experience supporting large-scale inference workloads across text, image, and video processing GPU optimization, autoscaling, A/B testing, model evaluation, and drift detection Building enterprise MLOps platforms supporting high-volume production AI workloads Show more Show less