MLOps Engineer - Azure AI Production & Edge Deployment

AgileEngine, LLC.; Texas; Hybride; USD 130 000 - 180 000; Plein temps

Avantages offerts par ce poste

Professional growth

Competitive pay

Exciting projects

Flextime

Résumé du poste

AgileEngine, LLC. is seeking an experienced 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 with a 3-day-per-week office requirement and flexible remote options.

You will own end-to-end model serving behind REST/GRPC endpoints, manage pipelines, and collaborate with data scientists to ensure production-grade reliability and performance.

Qualifications

• Must have 3+ years of experience in MLOps, DevOps, data engineering, ML, or software engineering.

Responsabilités

• - Lead end-to-end deployment of AI/ML models into production with robust monitoring and telemetry.
• - Build and maintain scalable ingestion and inference pipelines for high-volume image streams on Azure.
• - Optimize GPU compute and reduce infrastructure costs (quantization, pruning, TensorRT/ONNX, dynamic batching).
• - Architect edge and cloud deployment strategies for production CV workloads.
• - Ensure model versioning, experiment tracking, and reproducibility across production.

Connaissances

MLOps

DevOps

Azure

AKS

Docker

Kubernetes

CI/CD

Telemetry

Model serving

TorchServe

TensorRT

Pruning

Quantization

Formation

Degree in CS/Software Engineering

Outils

Azure Data Factory

Azure DevOps

AKS

Docker

Kubernetes

Description du poste

AgileEngine, LLC. is seeking an experienced 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 with a 3-day-per-week office requirement and flexible remote options.

You will own end-to-end model serving behind REST/GRPC endpoints, manage pipelines, and collaborate with data scientists to ensure production-grade reliability and performance.