MLOps Engineer ID72409

AgileEngine•Tallahassee, 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 is based in Dallas, TX, with office attendance three days per week; candidates must be based in Dallas or open to relocating. The engineer will own the full lifecycle transition from experimentation to reliable production deployment, build and scale image ingestion and inference pipelines, optimize GPU resources and infrastructure costs, and support cloud-based inference environments with secondary support for edge GPU deployments. The role involves close collaboration with data scientists and AI researchers to make experimental models production-ready.

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 US; the company cannot sponsor or take over employment visa sponsorship.
Must be based in Dallas, TX, or open to relocating to Dallas, and 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, and end-to-end backend pipeline ownership.
Exposure to experiment tracking, model registry/versioning, drift detection, and production telemetry/monitoring.
Working exposure to computer vision or image data in production.
Strong practical experience with Azure, specifically 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 infrastructure costs, maximizing camera throughput per GPU using 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, visibility dashboards, and data and concept drift detection to support model accuracy and system reliability.
Manage experiment tracking and model versioning to ensure model reproducibility and traceability in production.
Collaborate with data scientists and AI researchers to translate experimental models into robust, production-ready solutions.