MLOps Engineer ID72409

AgileEngine•Boston, GA

9d•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 machine-learning experimentation to reliable, high-performance production deployments, including infrastructure, pipelines, model lifecycle management, monitoring, and GPU resource optimization. The position is based in Dallas, Texas, or requires willingness to relocate to Dallas, with office attendance three days per week. It supports large-scale cloud-based inference environments and, for specific use cases, edge GPU deployments. The engineer collaborates with data scientists and AI researchers to turn experimental models into production-ready solutions.

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; employer visa sponsorship or transfer is unavailable.
Must be 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, and 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.
Practical cloud experience focused on Azure, including AKS, Azure DevOps, the Azure data stack, and managing or provisioning 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 efficiency and infrastructure costs by maximizing camera throughput per GPU.
Apply model and inference optimizations, including quantization (FP16/INT8), pruning, TensorRT/ONNX conversion, dynamic batching, and GPU autoscaling on AKS.
Own the 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 reproducibility and traceability of production models.
Collaborate with data scientists and AI researchers to translate experimental models into robust, production-ready solutions.