MLOps Engineer ID72409

AgileEngine • San Francisco, CA

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 is based in or requires relocation to Dallas, Texas, with office attendance three days per week, and offers flexible scheduling with remote and office options.

The engineer will own the transition from machine-learning experimentation to reliable, high-performance production deployments. The role focuses on optimizing GPU efficiency and infrastructure costs, building Azure-based image ingestion and inference pipelines, managing cloud and edge deployments, and ensuring production model reliability through monitoring and lifecycle management. The engineer will also collaborate with data scientists and AI researchers to deliver production-ready solutions.

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.
• 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; able to attend the office three days per week.
• 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 and versioning, drift detection, and production telemetry and 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.
• Solid conceptual understanding of AI/ML fundamentals for communicating, troubleshooting, and collaborating with applied model developers.
• Upper-intermediate English proficiency.

Responsibilities

• Optimize GPU efficiency and infrastructure costs, maximizing camera throughput per GPU using techniques such as quantization (FP16/INT8), pruning, TensorRT/ONNX conversion, dynamic batching, and GPU autoscaling on AKS.
• Own the complete lifecycle transition from AI/ML experimentation to reliable, high-performance production deployment.
• Build, maintain, and scale image ingestion and inference pipelines 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, visibility dashboards, and 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.
• Partner with data scientists and AI researchers to turn experimental models into robust, production-ready solutions.

Benefits

• Mentorship, TechTalks, and personalized growth roadmaps.
• USD-based pay with education, fitness, and team activity budgets.
• Exciting projects with 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