MLOps Engineer ID72409

AgileEngine•Orlando, FL

9d•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 position is based in Dallas, TX, or requires willingness to relocate there, with office attendance three days per week. The role also offers flexible scheduling and remote and office options. The engineer will own the transition from machine-learning experimentation to reliable, high-performance production deployment. Responsibilities span GPU and infrastructure cost optimization, cloud and edge inference, image ingestion and inference pipelines, CI/CD, monitoring, drift detection, and model lifecycle management. The role involves close collaboration with data scientists and AI researchers to productionize experimental models.

Requirements

At least 3 years of professional experience in MLOps, DevOps, Data Engineering, Machine Learning, or Software Engineering.
A 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, TX, or be open to relocating to Dallas; 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, and 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, including AKS, Azure DevOps, the Azure data stack, and 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 inference optimization techniques, 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 for specific use cases.
Implement production monitoring systems, build visibility dashboards, and configure 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 turn experimental models into robust, production-ready solutions.