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); * Proven experience working with frameworks like LangChain, LangGraph, LlamaIndex; * Hands-on experience implementing RAG; * Practical experience in designing and building agent-based AI systems; * 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.
