Since our founding in 2019, Trayvisor has been at the forefront of artificial intelligence applied to catering restaurants. Our innovations include:
🍽️ Self-Checkout API: Reducing wait times through deep learning for an optimal customer experience.
🏥 API for Healthcare Facilities: Precise analysis of patient intake, fighting malnutrition in the medical field.
We are growing rapidly and are looking to strengthen our technical team with a passionate Backend Engineer.
As a Senior Backend Engineer (Python), you will play a key role in developing and enhancing our API to power AI-driven solutions. Working closely with the frontend engineer, DevOps, and machine learning engineers, you will design and implement scalable, high-performance backend systems. Your contributions will directly impact the user experience and the efficiency of our platform.
Your Responsibilities:
Design, develop, and maintain high-quality APIs and backend systems using Python (FastAPI).
Collaborate with the frontend engineer to ensure seamless integration between the frontend and backend.
Work alongside the DevOps team to optimize backend performance and deployment processes.
Integrate machine learning models developed by the ML team into the backend infrastructure.
Write clean, maintainable, and well-documented code following best practices.
Participate in code reviews to ensure code quality and knowledge sharing.
Contribute to system architecture discussions and propose innovative solutions.
Why Join Us?
Innovation: Work in a growing and innovative company.
Professional Development: Numerous opportunities for professional and career development.
Flexibility: Full remote work is possible or in our offices in Paris.
5+ years of experience in backend development with Python.
Expertise in FastAPI or similar frameworks for API development.
Familiarity with asynchronous programming paradigms (e.g., asyncio).
Strong knowledge of PostgreSQL and database optimization.
Solid understanding of software development principles, including testing and debugging.
Knowledge or experience with distributed task queues / workflow orchestrators such as Celery, Ray and Prefect
Nice to have:
Experience in integrating machine learning models into production environments.
Knowledge of performance monitoring tools and optimization techniques.
Familiarity with cloud platforms (GCP / AWS) and containerization tools like Docker.
Initial Chat: 30-45 minutes with a member of our team.
Technical Test: Show us your skills.
Technical Interview: In-depth discussion about your skills and experience.
Final Chat: Last step before final validation.
