MLOps Engineer ID72409

AgileEngine•Houston, TX

9d•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, including cloud-based inference environments and some support for edge GPU deployments. The position is based in or requires relocation to Dallas, Texas, with office attendance three days per week; the posting also describes flexible scheduling and remote and office options.

Requirements

Be authorized to work for any employer in the United States; the company cannot sponsor or take over employment visa sponsorship.
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, with end-to-end backend pipeline ownership.
Exposure to experiment tracking, model registry and versioning, drift detection, and production telemetry or 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, including 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 to communicate, troubleshoot, and collaborate with applied model developers.
Upper-intermediate English proficiency.

Responsibilities

Optimize GPU efficiency and infrastructure costs by increasing 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 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, 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.
Collaborate with data scientists and AI researchers to turn experimental models into production-ready solutions.