Job Description
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.
