MLOps Engineer: Azure AI Pipelines & Edge Inference

AgileEngine, LLC.; Texas City (TX); Hybride; USD 120 000 - 180 000; Plein temps

Avantages offerts par ce poste

Professional growth

Competitive compensation (USD-based)

Exciting projects

Flextime - remote and office options

Résumé du poste

AgileEngine, LLC. is seeking an experienced MLOps Engineer to deploy, scale, and monitor computer vision models in production on Azure.

The role focuses on processing real-time image streams from edge cameras, with emphasis on end-to-end deployment, model lifecycle management, and robust CI/CD pipelines. The ideal candidate has 3+ years in MLOps/DevOps, strong Azure experience (AKS, Data Stack, DevOps), and hands-on work with GPU resources.

Qualifications

• 3+ years of MLOps/DevOps/ML or related experience.
• Degree in CS, Software Engineering, or related field or equivalent experience.
• Must be based in Dallas, TX or open to relocation; office attendance 3 days/week.

Responsabilités

• Lead production model deployment and end-to-end ML pipelines.
• Optimize GPU compute and reduce infra costs for camera streams.
• Build robust ingestion and inference pipelines on Azure; CI/CD pipelines.
• Deploy and manage edge and cloud inference environments.
• Implement production monitoring, drift detection, and telemetry.
• Manage experiment tracking and model versioning for reproducibility.
• Collaborate with data scientists to translate experiments into production.

Connaissances

MLOps

DevOps

Data Engineering

Machine Learning

Software Engineering

Formation

Bachelor's degree in CS/Software Engineering or related

Outils

Azure AKS

Azure DevOps

Azure Data Stack

Docker

Kubernetes

Triton Inference Server

TorchServe

KServe

Azure ML endpoints

Description du poste

AgileEngine, LLC. is seeking an experienced MLOps Engineer to deploy, scale, and monitor computer vision models in production on Azure.

The role focuses on processing real-time image streams from edge cameras, with emphasis on end-to-end deployment, model lifecycle management, and robust CI/CD pipelines. The ideal candidate has 3+ years in MLOps/DevOps, strong Azure experience (AKS, Data Stack, DevOps), and hands-on work with GPU resources.