Release Engineer

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-driven technology, representing the pinnacle of machine learning, GenAI, and computer vision within the automotive sector.

With close to $400M in funding and strategic partnerships with industry giants such as Amazon, General Motors, Volvo, and CarMax, UVeye stands at the forefront of automotive technological advancement. Our growing global team of over 200 employees is committed to creating a workplace that celebrates diversity and encourages teamwork. Our drive for innovation and pursuit of excellence are deeply embedded in our vibrant company culture, ensuring that each individual's efforts are recognized and valued as we unite to build a safer automotive world.

We are seeking a highly motivated and skilled Release Engineer to join our AIOps group.

In this role, you'll play a critical part in bridging the gap between development and operations, ensuring the seamless qualification, deployment, and monitoring of our AI algorithms and infrastructure, and be responsible for the end-to-end operationalization of our core technology.

A day in the life and how you’ll make an impact:

• Manage the end-to-end release process of machine learning algorithms and infrastructure components, from qualification through deployment.
• Validate and test new algorithm releases to ensure they meet performance, stability, and compliance standards.
• Create and execute deployment plans across various environments (staging, production), ensuring minimal risk and downtime.
• Collaborate closely with AI researchers, MLOps, and software engineers to understand release requirements, share feedback, and resolve pre-release issues.
• Identify and drive automation opportunities within the release pipeline to improve efficiency, reliability, and traceability.
• Oversee updates to infrastructure components, ensuring compatibility and performance across systems.
• Monitor deployments, proactively identify issues related to model behavior or infrastructure anomalies, and drive resolution with relevant teams.
• Maintain clear and accurate release documentation, including version history, deployment notes, and incident reports.
  
  

• Bachelor's degree in Computer Science, Software Engineering, or industry equivalent.
• 2+ years of experience in QA & Automation
• Proficiency in scripting languages (e.g., Python, Bash).
• Experience with containerization technologies (e.g., Docker, Kubernetes).
• Familiarity with CI/CD pipelines (e.g., GitLab CI/CD, Jenkins).
• Experience with cloud platforms (e.g., AWS, GCP).
• Familiarity with monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack).
• Excellent problem-solving skills and attention to detail.
• Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams.
  
  

• Strong understanding of the machine learning lifecycle, from experimentation to deployment and monitoring.
• Experience with specific MLOps platforms or tools.
• Experience in a fast-paced startup environment.