MLOps Engineer ID72409

AgileEngine•Tampa, 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 role owns the full path from machine-learning experimentation to reliable production deployment, including inference and ingestion pipelines, GPU infrastructure, model lifecycle management, and production monitoring. The position is based in or requires relocation to Dallas, Texas, with office attendance three days per week; the posting also mentions remote and office options.

Requirements

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.
A degree in Computer Science, Software Engineering, or a related technical discipline, or equivalent practical experience.
Based in Dallas, Texas, or 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, including end-to-end backend pipeline ownership.
Exposure to experiment tracking, model registries 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, particularly AKS, Azure DevOps, the Azure data stack, and 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 proficiency.

Responsibilities

Optimize GPU compute efficiency and reduce infrastructure costs by maximizing 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 in specific use cases.
Implement production monitoring systems, build visibility dashboards, and set up data and concept drift detection to maintain 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 turn experimental models into robust, production-ready solutions.