MLOps Engineer ID72409

AgileEngine•Irving, IL

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 AI/ML experimentation to reliable, high-performance production deployment and includes building robust ingestion and inference pipelines, optimizing GPU use and infrastructure costs, and maintaining monitoring and model lifecycle systems. The position is based in Dallas, Texas, or requires willingness to relocate there, with office attendance three days per week. The role focuses on cloud-based inference environments, with secondary support for edge GPU deployment in specific use cases. It involves close collaboration with data scientists and AI researchers.

Requirements

Be authorized to work for any employer in the United States; employer visa sponsorship or takeover of existing employment visa sponsorship is not available.
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.
Be based in Dallas, Texas, or be willing to relocate to Dallas; 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, and 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, and the Azure data stack, including 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

Lead GPU compute efficiency and infrastructure cost-reduction strategies by optimizing models to maximize camera throughput per GPU.
Apply 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 across 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.
Partner with data scientists and AI researchers to translate experimental models into robust, production-ready solutions.