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At UVeye, we're on a mission to redefine vehicle safety and reliability on a global scale. Founded in 2016, we have pioneered the world's first fully automated suite of vehicle inspection systems. At the heart of this innovation lies our advanced AI-centric technology, representing the pinnacle of computer vision, machine learning, and generative AI within the automotive sector. With over $380M in funding and strategic partnerships with industry giants such as Toyota, Amazon, General Motors, Volvo, and Hertz, our technology is utilized in manufacturing plants, dealerships, wholesale auctions, delivery fleets, seaports, and more. Our growing global team of over 200 employees is committed to creating a workplace that celebrates diversity, encourages teamwork, and strives for excellence.
We are looking for a driven, systems-minded Release Engineer to join our AI-Ops team. In this role, you will be the execution layer of our delivery pipeline—the technical gatekeeper who owns the safe, predictable deployment of software and AI models to global edge and cloud systems. You will balance high-speed deployment velocity with rock-solid operational stability. But you won't just be deploying code; you'll be acting as an internal project manager, driving our organizational roadmap by building Agentic AI tools and automating processes to scale our delivery capabilities continuously.
A day in the life and how you’ll make an impact:
* Act as the technical gatekeeper, validating and transitioning versions through strict release gates. Enforce rigorous governance and ensure strict "Definition of Done" criteria are met.
* Lead risk-mitigated rollouts across diverse global hardware environments.
* Monitor real-time deployment performance, Quality of Service (QoS), and algorithmic accuracy, making decisive, crisis-resilient calls to proceed, pause, or rollback to prevent regressions.
* Define and execute comprehensive test plans that verify cross-team dependencies. Validate that new versions meet detection accuracy requirements without degrading infrastructure.
* Triage complex production failures. Look beyond immediate issues to identify root causes using system metrics, logs, and container states, delivering actionable evidence to R&D.
* Build and integrate Agentic AI and LLM-based tools to accelerate log analysis, risk assessment, and deployment troubleshooting.
* Architect automated workflows to eliminate manual overhead and enhance system observability with robust monitors and dashboards.