MLOps Engineer ID72409

AgileEngine•Jersey City, NJ

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 or requires relocation to Dallas, Texas, with office attendance three days per week. The engineer will own the transition from machine-learning experimentation to reliable production deployment, including model serving, high-volume image ingestion and inference pipelines, GPU resource optimization, and production monitoring. The position works closely with data scientists and AI researchers to make experimental models production-ready, and provides secondary support for edge GPU deployment in specific use cases.

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 US employer; the company is unable to sponsor or take over employment visa sponsorship.
Must be based in Dallas, Texas, or be open to relocating there; 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, including ownership of end-to-end backend pipelines.
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.
Strong practical experience with Azure cloud environments, particularly AKS, Azure DevOps, and the Azure data stack, including 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 techniques such as 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 maintain model accuracy and system reliability.
Manage experiment tracking and model versioning to ensure production models are reproducible and traceable.
Collaborate with data scientists and AI researchers to turn experimental models into production-ready solutions.