MLOps Engineer ID72409

AgileEngine; Irving (TX); On-site; USD 120,000 - 165,000; Full time

Benefits offered by this job

Professional growth

Competitive compensation

Exciting projects

Flextime

Job summary

AgileEngine, LLC. in Irving, TX is seeking an experienced MLOps Engineer to deploy, scale, and monitor computer vision models in production on Azure. You will work with real-time image streams from edge cameras.

The role requires strong backend and cloud skills, including AKS, Azure DevOps, and Docker/Kubernetes, plus production CV exposure. Office attendance: 3 days per week.

Qualifications

• 3+ years in MLOps, DevOps, data engineering, ML, or software engineering.
• Degree in CS, software engineering, or related field.
• Based in Dallas, TX, or willing to relocate; office 3 days/week.
• Production exposure to CV/image data.
• Proficient with Azure, AKS, Azure DevOps, and Azure data stack.

Responsibilities

• Optimize GPU compute cost and throughput per GPU on AKS.
• Own end-to-end deployment lifecycle from experimentation to production.
• Build and scale ingestion and inference pipelines using Azure Data Factory and cloud storage.
• Manage edge and cloud deployment for large-scale inference.
• Implement production monitoring and drift detection.
• Manage model lifecycle, experiment tracking, and versioning.
• Collaborate with data scientists to translate experiments into production.

Skills

MLOps experience

DevOps

Data engineering

Machine learning

Software engineering

Education

Degree in Computer Science or Software Engineering

Tools

AKS (Azure Kubernetes Service)

Azure DevOps

Azure data stack

Docker

Kubernetes

Job description

Irving, United States

WHY JOIN US

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!

ABOUT THE ROLE

We are looking for 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.

MUST HAVES

• You must be authorized to work for ANY employer in the US (e.g., Green card holders, TN visa holders, GC EAD, H4 EAD, U4U with EAD), as we are unable to sponsor or take over employment visa sponsorship at this time;
• 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.
• MLOps Core: Production model serving (Triton Inference Server, TorchServe, KServe, or Azure ML endpoints) behind REST/gRPC APIs, with end-to-end backend pipeline ownership. Exposure to tracking, model registry/versioning, drift detection, and production telemetry/monitoring.
• Computer Vision: Working exposure to CV/image data in production.
• Infrastructure & Cloud: Strong practical experience navigating cloud environments, strictly focused on Azure, specifically using AKS for deployment, Azure DevOps, and the Azure data stack, alongside managing/provisioning GPU compute resources.
• DevOps & Automation: Deep understanding of containerization (e.g., Docker and Kubernetes) and designing robust CI/CD pipelines for automated deployments.
• AI/ML Foundation: A solid conceptual understanding of AI/ML fundamentals to effectively communicate, troubleshoot, and collaborate with applied model developers.

WHAT YOU WILL DO

• Resource & Cost Optimization: Lead GPU compute efficiency and infrastructure cost-reduction strategies by optimizing models to maximize camera throughput per GPU. Apply optimization techniques including quantization (FP16/INT8), pruning, TensorRT/ONNX conversion, dynamic batching, and GPU autoscaling on AKS.
• 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.

PERKS AND BENEFITS

• Professional growth: Mentorship, TechTalks, and personalized growth roadmaps.
• Competitive compensation: USD-based pay with education, fitness, and team activity budgets.
• Exciting projects: Modern solutions with Fortune 500 and top product companies.
• Flextime: Flexible schedule with remote and office options.