MLOps Engineer: Azure GPU Inference in Production

AgileEngine, LLC.; Port Charlotte (FL); Hybrid; USD 140,000 - 200,000; Full time

Benefits offered by this job

Professional growth

Competitive compensation

Exciting projects

Flextime

Job summary

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 requires 3+ years in MLOps/DevOps and a solid CS background, with preferred relocation to Dallas and a 3-days-per-week office attendance. You will own end-to-end deployment, build robust pipelines, optimize GPU compute, and manage model versioning.

Qualifications

• 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 based in Dallas, TX, or open to relocating to Dallas. Office attendance: 3 days/week.

Responsibilities

• Resource & Cost Optimization: Lead GPU compute efficiency and infrastructure cost-reduction strategies by optimizing models to maximize camera throughput per GPU.
• End-to-End Deployment: Own the complete lifecycle transition from AI/ML experimentation to reliable, high-performance production deployment.
• Infrastructure & Pipelines: Build, maintain, and scale robust ingestion and inference pipelines for high-volume image streams using Azure Data Factory, cloud storage, and automated CI/CD workflows.
• Edge & Cloud Deployment: Architect and manage large-scale cloud-based model inference environments, with secondary support for edge GPU deployment across specific edge use cases.
• System Stability & Monitoring: Implement robust production monitoring systems, build visibility dashboards, and set up data and concept drift detection to ensure ongoing model accuracy and system reliability.
• Model Lifecycle Management: Manage experiment tracking and model versioning to ensure full reproducibility and traceability of all models in production.
• Cross-Functional Collaboration: Partner closely with data scientists and AI researchers to translate experimental models into robust, production‑ready solutions.

Skills

MLOps

DevOps

Data Engineering

Machine Learning

Software Engineering

Education

CS degree or equivalent

Tools

Azure AKS

Azure DevOps

Azure data stack

Docker

Kubernetes

Triton Inference Server

TorchServe

KServe

Azure ML endpoints

Job description

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 requires 3+ years in MLOps/DevOps and a solid CS background, with preferred relocation to Dallas and a 3-days-per-week office attendance. You will own end-to-end deployment, build robust pipelines, optimize GPU compute, and manage model versioning.