MLOps Engineer ID72409

AgileEngine; Austin (TX); On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Flextime with on-site option

Competitive USD-based compensation

Job summary

AgileEngine, LLC. is seeking a Middle/Senior MLOps Engineer in Dallas, TX, to move computer vision models from experimentation to reliable production.

The role spans ML infrastructure, GPU optimization, and deployment across cloud and edge environments, with Docker, Kubernetes, and CI/CD driving reproducible pipelines and lifecycle controls. You will lead end-to-end deployment, build scalable pipelines, and partner with data scientists to deliver production-ready CV solutions while balancing

Qualifications

• Authorized to work in the US without sponsorship.
• 3+ years of professional experience in MLOps, DevOps, Data Engineering, ML, or Software Engineering.
• Degree in Computer Science, Software Engineering, or related technical discipline (or equivalent).
• US-based Dallas, TX residents on-site.
• Role focused on Computer Vision and GPU engineering; CV/ML infra experience preferred over LLM/Chatbot work.
• Hands-on MLOps: experiment tracking, model registry/versioning, drift detection, production monitoring.
• CV experience: training CV models on GPUs, dataset management, CV-specific monitoring.
• Cloud & infrastructure: practical cloud navigation and GPU provisioning.
• DevOps & Automation: Docker, Kubernetes, and robust CI/CD pipelines for automated deployments.
• AI/ML Foundation: solid understanding of AI/ML fundamentals to collaborate with model developers.
• Upper-intermediate English level.

Responsibilities

• Own the complete lifecycle transition from AI/ML experimentation to reliable production deployment.
• Build, maintain, and scale infrastructure, automation, and CI/CD workflows for rapid model deployment.
• Implement robust production monitoring, dashboards, and drift detection for model accuracy and reliability.
• Manage experiment tracking and model versioning to ensure reproducibility and traceability.
• Partner with data scientists and AI researchers to translate experiments into production-ready solutions.
• Optimize cloud environments and GPU compute resources for performance and cost efficiency.
• Deploy CV models to cloud and edge environments with GPU optimization.

Skills

MLOps core

Computer vision pipelines

GPU training

Dataset management

Cloud & GPU provisioning

CI/CD pipelines

Docker

Kubernetes

Edge deployment

English communication

3+ years experience

Education

B.S. in CS/Software Engineering or related field

Tools

Docker

Kubernetes

Cloud platforms

CI/CD tooling

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 a Middle/Senior MLOps Engineer to move computer vision models from experimentation into reliable production. The role combines ML infrastructure, GPU optimization, and deployment across cloud and edge environments. You will build reproducible pipelines, monitoring, and lifecycle controls for image and video workloads using Docker, Kubernetes, and CI/CD.

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).
• Engineers located in the US must reside in Dallas, TX, and be willing to work onsite.
• Note: This role is strictly focused on Computer Vision and GPU engineering. Profiles heavily focused on LLMs, RAG pipelines, chatbots, or prompt engineering will not be a fit unless accompanied by solid, practical CV MLOps experience.
• MLOps Core: Hands-on experience with experiment tracking, model registry/versioning, drift detection, and production monitoring.
• Computer Vision: Strong hands-on experience in CV pipelines, including training computer vision models on GPUs, dataset management, and infrastructure monitoring specific to CV model quality/drift.
• Infrastructure & Cloud: Strong practical experience navigating cloud environments and 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.
• Upper-intermediate English level.

What You Will Do

• 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 the infrastructure, automation, and CI/CD workflows necessary for rapid and efficient model deployment, including large-scale image/video data ingestion, dataset versioning, and managing manual/automated image annotation workflows.
• 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.
• Resource & Cost Optimization: Manage cloud environments and GPU compute resources to ensure systems are not only highly scalable but also cost-effective, focusing on model optimization for heavy GPU workloads, including latency, throughput, batching, and GPU memory utilization.
• Edge & Cloud Deployment: Deploy computer vision models to cloud environments and optimize them for Edge GPU deployment in specific use cases.

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.