MLOps Engineer ID72409

AgileEngine•Miami, FL

6d•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 machine-learning experimentation to reliable, high-performance production deployment, including cloud-based inference environments and secondary support for edge GPU deployments. The engineer will optimize GPU efficiency and infrastructure costs, build and scale image ingestion and inference pipelines, and maintain model lifecycle practices such as experiment tracking and versioning. The role works closely with data scientists and AI researchers to deliver production-ready solutions. 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.
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 is unable to 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.
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 and 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, optimizing models to maximize camera throughput per GPU.
Apply model and inference optimization techniques, including FP16/INT8 quantization, 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 deployments for 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 translate experimental models into robust, production-ready solutions.