MLOps Engineer ID72409

AgileEngine•Fort Lauderdale, FL

5d•Hybrid

About The Position

AgileEngine is hiring 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. The engineer will own the production lifecycle of models, from experimentation through reliable deployment, and build high-volume ingestion and inference pipelines using Azure services. The role focuses on optimizing GPU usage and infrastructure costs, operating cloud inference environments, monitoring system and model performance, managing model versions and experiments, and collaborating with data scientists and AI researchers. Flexible scheduling with remote and office options is offered.

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.
Strong practical experience with Azure cloud environments, specifically AKS, Azure DevOps, and the Azure data stack, including management or provisioning of 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 to communicate, troubleshoot, and collaborate with applied model developers.
Upper-intermediate English level.

Responsibilities

Optimize GPU compute efficiency and infrastructure costs, including maximizing camera throughput per GPU.
Apply model and inference optimization techniques such as FP16/INT8 quantization, pruning, TensorRT/ONNX conversion, dynamic batching, and GPU autoscaling on AKS.
Own the transition of models 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, visibility dashboards, and data and concept drift detection to maintain model accuracy and system reliability.
Manage experiment tracking and model versioning for production-model reproducibility and traceability.
Collaborate with data scientists and AI researchers to turn experimental models into production-ready solutions.