MLOps Engineer ID72409

AgileEngine•Port Charlotte, FL

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 production deployment and includes optimizing GPU efficiency and infrastructure costs, maintaining high-volume image ingestion and inference pipelines, and supporting cloud-based inference environments with some edge GPU deployment. The engineer will build production monitoring and visibility systems, detect data and concept drift, and manage experiment tracking and model versioning for reproducibility. The role works closely with data scientists and AI researchers to turn experimental models into production-ready solutions. The position is based in Dallas, TX, or requires willingness to relocate to Dallas, with office attendance three days per week; flexible scheduling with remote and office options is 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 employer cannot sponsor or take over employment visa sponsorship.
Must be based in Dallas, TX, or be willing to relocate 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, 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.
Practical experience with Azure cloud environments, specifically AKS, Azure DevOps, the Azure data stack, and provisioning or managing 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

Optimize GPU compute efficiency and reduce infrastructure costs by maximizing camera throughput per GPU.
Apply model optimization techniques including quantization (FP16/INT8), pruning, TensorRT/ONNX conversion, dynamic batching, and GPU autoscaling on AKS.
Own the end-to-end 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, create 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 production models are reproducible and traceable.
Collaborate with data scientists and AI researchers to translate experimental models into robust, production-ready solutions.