MLOps Engineer ID72409

AgileEngine; Blacksburg (SC); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Professional growth

Competitive compensation

A selection of exciting projects

Flextime

Job summary

AgileEngine is seeking a Middle/Senior MLOps Engineer to move computer vision models from experimentation into reliable production. You will design reproducible pipelines, monitor lifecycles, and optimize GPU workloads across cloud and edge environments.

The role emphasizes CV pipelines, GPU optimization, and CI/CD with Docker and Kubernetes. You will collaborate with data scientists and researchers to deliver production-ready ML solutions in a fast-paced, people-first culture.

Qualifications

• 3+ years of professional experience in MLOps, DevOps, Data Engineering, ML, or Software Engineering.
• Degree in Computer Science, Software Engineering, or related discipline (or equivalent practical experience).
• Engineers located in the US must reside in Dallas, TX and be willing to work onsite.

Responsibilities

• Own the end-to-end deployment lifecycle from ML experiments to production.
• Build and maintain infrastructure, pipelines, and CI/CD for rapid model deployment.
• Implement production monitoring, data drift detection, and system reliability measures.

Skills

MLOps

DevOps

Data Engineering

Machine Learning

Software Engineering

Education

Degree in CS/Software Eng or related

Tools

Docker

Kubernetes

CI/CD pipelines

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.

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.

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.

PERKS AND BENEFITS

• -Professional growth : Accelerate your professional journey with mentorship, TechTalks, and personalized growth roadmaps.
• -Competitive compensation : We match your ever-growing skills, talent, and contributions with competitive USD-based compensation and budgets for education, fitness, and team activities.
• -A selection of exciting projects : Join projects with modern solutions development and top-tier clients that include Fortune 500 enterprises and leading product brands.
• -Flextime : Tailor your schedule for an optimal work-life balance, by having the options of working from home and going to the office – whatever makes you the happiest and most productive.