MLOps Engineer ID72409

AgileEngine•New York, NY

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 transition from AI/ML experimentation to reliable, high-performance production deployments and includes responsibility for model serving, infrastructure, pipelines, monitoring, and lifecycle management. The engineer will optimize GPU use and infrastructure costs, build and maintain ingestion and inference pipelines for high-volume image streams, and manage large-scale cloud inference environments, with secondary support for edge GPU deployment in specific use cases. The position collaborates closely with data scientists and AI researchers to turn experimental models into production-ready solutions. The role is based in Dallas, Texas, or requires willingness to relocate there, with office attendance three days per week. The posting also describes flexible scheduling and 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 be willing to relocate to Dallas; office attendance is required 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 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, including AKS, Azure DevOps, the Azure data stack, and management or provisioning of 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 proficiency.

Responsibilities

Lead GPU compute efficiency and infrastructure cost-reduction strategies, optimizing models to maximize camera throughput per GPU.
Apply model and inference optimization techniques, including 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 for specific use cases.
Implement production monitoring systems, build visibility dashboards, and establish data and concept drift detection 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 translate experimental models into robust, production-ready solutions.