MLOps Engineer ID72409

AgileEngine•West Palm Beach, 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, or requires willingness to relocate to Dallas, with office attendance three days per week. The engineer will own the transition from machine-learning experimentation to reliable production systems and collaborate with data scientists and AI researchers to deliver production-ready solutions. The position focuses on optimizing GPU compute efficiency and infrastructure costs, building high-volume image ingestion and inference pipelines, and managing cloud-based inference environments with secondary support for selected edge GPU deployments. It also includes production monitoring, drift detection, model experiment tracking, and version management. AgileEngine describes its culture as people-first and highlights professional growth, competitive compensation, exciting projects, and flexible scheduling with remote and office options.

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, Texas, or willing to relocate to Dallas; must attend the office 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 model tracking, model registry/versioning, drift detection, and production telemetry or monitoring.
Working exposure to computer-vision or image data in production.
Strong practical experience with Azure cloud environments, 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.
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 optimization techniques such as FP16/INT8 quantization, 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, create visibility dashboards, and establish 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.
Collaborate with data scientists and AI researchers to translate experimental models into robust, production-ready solutions.