MLOps Engineer ID72409

AgileEngine • Richmond, VA

5d • Hybrid

About The Position

AgileEngine is hiring 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 lifecycle transition from AI/ML experimentation to reliable, high-performance production deployment, with a focus on cloud-based inference and GPU efficiency. The engineer will build and maintain high-volume image ingestion and inference pipelines, optimize GPU resources and infrastructure costs, establish production monitoring and model lifecycle management, and collaborate with data scientists and AI researchers. The position is based in or requires relocation to Dallas, Texas, with office attendance three days per week; flexible scheduling and remote and office options are also mentioned.

Requirements

• At least 3 years of professional experience in MLOps, DevOps, Data Engineering, Machine Learning, or Software Engineering.
• A degree in Computer Science, Software Engineering, or a related technical discipline, or equivalent practical experience.
• Authorized to work for any employer in the United States; the company cannot sponsor or take over employment visa sponsorship.
• Based in Dallas, Texas, or willing to relocate to Dallas; attend the office three days per week.
• Practical experience with production model serving using Triton Inference Server, TorchServe, KServe, or Azure ML endpoints behind REST/gRPC APIs, including end-to-end backend pipeline ownership.
• Exposure to experiment tracking, model registry/versioning, drift detection, and production telemetry/monitoring.
• Working exposure to computer vision or image data in production.
• Strong practical experience with Azure cloud environments, specifically AKS, Azure DevOps, the Azure data stack, and managing or provisioning GPU compute resources.
• Deep understanding of containerization, such as Docker and Kubernetes, and designing robust CI/CD pipelines 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 using quantization (FP16/INT8), pruning, TensorRT/ONNX conversion, dynamic batching, and GPU autoscaling on AKS.
• Own the complete 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 for specific use cases.
• Implement production monitoring systems, create visibility dashboards, and configure data and concept drift detection to support 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 compensation.
• Education, fitness, and team activity budgets.
• Exciting projects with 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