MLOps Engineer ID72409

AgileEngine • Baltimore, MD

5d • Hybrid

About The Position

AgileEngine is seeking an MLOps Engineer to deploy, scale, and monitor computer vision models in production on Azure, processing real-time image streams from thousands of edge cameras. The role owns the transition from machine-learning experimentation to reliable, high-performance production deployment, with emphasis on cloud inference environments and GPU efficiency. The position is based in or requires relocation to Dallas, Texas, with office attendance three days per week; the posting also mentions flexible scheduling and remote and office options.

The engineer will build and maintain high-volume image ingestion and inference pipelines, optimize model and GPU performance, establish production monitoring and drift detection, and manage experiment tracking and model versioning. The role works closely with data scientists and AI researchers to deliver production-ready solutions. Secondary support for edge GPU deployment is included for specific use cases.

Requirements

• At least 3 years of professional experience in MLOps, DevOps, Data Engineering, Machine Learning, or Software Engineering.
• Degree in Computer Science, Software Engineering, or a related technical discipline, or equivalent practical experience.
• Must be authorized to work for any employer in the United States; the employer cannot sponsor or take over employment visa sponsorship.
• Must be based in Dallas, Texas, or be open to relocating there; office attendance is required three days per week.
• Production model-serving experience with Triton Inference Server, TorchServe, KServe, or Azure ML endpoints behind REST or gRPC APIs, including end-to-end backend pipeline ownership.
• Exposure to model tracking, model registries and versioning, drift detection, and production telemetry or monitoring.
• Working exposure to computer vision or image data in production.
• Strong practical experience with Azure cloud environments, specifically AKS, Azure DevOps, and the Azure data stack, including managing or provisioning GPU compute resources.
• Deep understanding of containerization, such as Docker and Kubernetes, and robust CI/CD pipeline design for automated deployments.
• Conceptual understanding of AI/ML fundamentals sufficient to communicate, troubleshoot, and collaborate with applied model developers.
• Upper-intermediate English level.

Responsibilities

• Optimize GPU compute efficiency and infrastructure costs, including maximizing camera throughput per GPU.
• Apply model optimization techniques such as FP16/INT8 quantization, pruning, TensorRT/ONNX conversion, dynamic batching, and GPU autoscaling on AKS.
• Own the lifecycle transition from AI/ML experimentation to reliable, high-performance production deployment.
• Build, maintain, and scale ingestion and inference pipelines for high-volume image streams using Azure Data Factory, cloud storage, and automated CI/CD workflows.
• Architect and manage large-scale cloud-based model inference environments, with secondary support for edge GPU deployment in specific use cases.
• Implement production monitoring, visibility dashboards, and data and concept drift detection to maintain model accuracy and system reliability.
• Manage experiment tracking and model versioning to ensure reproducibility and traceability of production models.
• Collaborate with data scientists and AI researchers to turn experimental models into robust, production-ready solutions.

Benefits

• Professional growth opportunities, including mentorship, TechTalks, and personalized growth roadmaps.
• USD-based competitive compensation with education, fitness, and team activity budgets.
• Projects involving modern solutions for Fortune 500 and top product companies.
• Flexible schedule with remote and office options.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level