Development & Enhancement of the RAG Platform
Design and implementation of Retrieval-Augmented Generation (RAG) architectures
Development of document ingestion pipelines (PDF parsing, chunking, metadata processing)
Implementation of embedding and vector search mechanisms
Optimization of prompt strategies and LLM workflows
Development of agentic workflows using LangGraph
Design and integration of AI-powered coding agents
Implementation of tool usage mechanisms and function calling
Automation of code generation processes
Design and implementation of knowledge graph structures (Neo4j)
Entity and relationship extraction from documents
Implementation of hybrid search architectures (vector search + graph traversal)
Integration of LLM providers (OpenAI, Anthropic, AWS Bedrock, Ollama)
Use of AWS services (S3, Lambda, Bedrock)
Containerization using Docker
Infrastructure automation with Terraform
Implementation of integration tests for AI systems
Evaluation of RAG systems (relevance, faithfulness)
Data model validation using Pydantic
Development of REST APIs with FastAPI

3+ years of experience in Python development
Strong proficiency in asynchronous programming (async/await, asyncio)
Experience with type hints and static typing
Solid experience with modern Python tooling, including: Package management: uv, Poetry Code quality tools: Ruff, MyPy Testing frameworks: pytest, pytest-asyncio

AI/ML Developer, RAG, AI Agents

Key skills