Devops Jobs in Chicago, IL

Job Description

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.

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.

- Upper-intermediate English level.

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.

Requirements
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 relocate to Dallas. Office attendance: 3 days/week. Note: This role is strictly focused on Computer Vision and GPU engineering. Profiles heavily focused on LLMs, RAG pipelines, chatbots, prompt engineering, or research-oriented CV profiles without large-scale production deployment experience will not be a fit unless accompanied by solid, practical CV MLOps experience. 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 (just 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, 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.