- GenAI platform currently under active development for intelligent job search
automation using LLMs and RAG architecture. - Designed with hexagonal (ports & adapters) architecture to ensure clean separation
between domain logic, AI workflows, and infrastructure components. - Ongoing implementation of multi-agent workflows for CV parsing, job matching,
cover letter generation, and recruiter-style feedback. - Development of retrieval-augmented generation (RAG) pipelines with vector
embeddings for semantic search over job offers and candidate profiles. - Backend architecture built with FastAPI, async processing, and modular AI services,
designed for experimentation and future production scaling. - Exploration of LLM orchestration patterns (prompt routing, tool calling, evaluation
loops) inspired by LangChain / LangGraph. - Dockerized development environment with CI-ready structure, focusing on testability,
traceability, and evolvability of AI systems.
Tech stack: Python, FastAPI, LLMs (OpenAI-compatible), RAG, Vector Search, Hexagonal
Architecture, Docker, CI/CD
- AI Developer
- Full Stack Developer