MLOps Engineer ID72409

AgileEngine•Dallas, TX

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 position is based in or requires relocation to Dallas, Texas, with office attendance three days per week. The role owns the transition from machine-learning experimentation to reliable production deployment and partners with data scientists and AI researchers to deliver production-ready solutions. The engineer will optimize GPU utilization and infrastructure costs, build and scale image ingestion and inference pipelines, manage cloud inference environments, and support selected edge GPU deployments. The role also covers production monitoring, drift detection, experiment tracking, model versioning, and reproducibility. AgileEngine describes its culture as people-first and offers mentorship, TechTalks, personalized growth roadmaps, competitive USD-based compensation, and budgets for education, fitness, and team activities, along with flexible scheduling and remote and office options.

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 company 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/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.
Practical experience with Azure cloud environments, specifically AKS, Azure DevOps, and the Azure data stack, as well as managing or provisioning GPU compute resources.
Strong 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 proficiency.

Responsibilities

Lead GPU compute-efficiency and infrastructure cost-reduction strategies, optimizing models to maximize camera throughput per GPU.
Apply model and serving optimizations including 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 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 systems, build visibility dashboards, and set up 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.