Middle+/Senior AI Engineer

What is the project idea?

We are looking for a talented AI Engineer specializing in LLM & Backend Integration/Development to join a fintech project. This is a position for a project focused on developing cutting edge fintech systems. You will be working on a platform that uses AI to provide intelligent insights and enhance the work environment on the fintech domain. You will integrate with a team of 2 AI Engineers and support both maintenance of existing systems and the development of new systems and implementing new business use-cases.

What is the team size and structure?

2 Senior AI Engineers and 1 Middle AI Engineer, PM, 2 Senior Back-end Engineers and 1 Middle Back-End Engineer.

How many stages of the interview are there?

– Interview with the Recruiter – up to 30 min.;

– Technical interview with Ralabs – up to 1 hour;

– Interview with the Client – up to 1 hour.

Requirements:

• At least 3 years of commercial experience as an AI Engineer or a similar role, preferably within the fintech industry;
• Strong proficiency in Python and hands-on experience on developing backend;
• Systems preferably  with FastAPI;
• Experience with Large Language Models (LLMs) and integrating with APIs(e.g., OpenAI, Hugging Face);
• Solid knowledge of relational databases, specifically Postgres, including schema design;
• Experience with cloud platforms, preferably Azure;
• Experience in designing and implementing REST APIs;
• Good problem solving skills;
• At least an Upper-Intermediate level of English.
  
  Responsibilities:
• Develop and integrate LLM-powered features to provide users with personalized financial insights and support.
• Set up and maintain a secure and scalable FastAPI backend service.
• Design and extend the Postgres database schema to manage user data.
• Implement and extend secure chat session management, message history storage, and user profile payload integration.
• Deploy and manage the application on Azure, ensuring high availability and reliability.
• Occasional work on traditional ML systems based on project needs.