MLOps Engineer ID72409

AgileEngine•Texas City, TX

9d•Hybrid

About The Position

AgileEngine is hiring an MLOps Engineer to deploy, scale, and monitor computer vision models in production on Azure. The role supports real-time image streams from thousands of edge cameras and owns the lifecycle transition from machine-learning experimentation to reliable, high-performance production deployment. The position is based in Dallas, TX, or requires willingness to relocate, with office attendance three days per week and flexible schedule options.

Requirements

Authorization to work for any US employer; the company is unable to 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, TX, or willing to relocate to Dallas; able to 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 ownership of end-to-end backend pipelines.
Exposure to model tracking, model registry and versioning, drift detection, and production telemetry or monitoring.
Working exposure to computer vision or image data in production.
Strong practical cloud experience focused on Azure, including 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

Lead GPU compute efficiency and infrastructure cost-reduction strategies, optimizing models to maximize camera throughput per GPU.
Apply optimization techniques including FP16/INT8 quantization, 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, visibility dashboards, data and concept drift detection, and measures to maintain 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 turn experimental models into robust, production-ready solutions.