Full Stack & AI Engineer – RAG, Agentic AI & Multi-Agent Systems

We are looking for an experienced Full Stack & AI Engineer to build intelligent applications powered by LLMs, Retrieval-Augmented Generation (RAG), and agentic AI workflows. You should have strong Python and full-stack development skills, along with hands-on experience designing and implementing AI agents, multi-agent systems, and production-ready AI applications. You will work on real-world projects involving private knowledge bases, intelligent automation, database querying, API integrations, and AI-powered business solutions. Key Responsibilities Design, develop, and deploy full-stack applications with integrated AI capabilities. Build RAG pipelines for querying private documents, databases, and enterprise knowledge bases. Develop AI agents capable of reasoning, tool calling, decision-making, and multi-step task execution. Implement multi-agent workflows using LangGraph, LangChain, CrewAI, or similar frameworks. Integrate LLMs such as OpenAI, Anthropic Claude, and Google Gemini. Develop REST APIs and backend services using Python and FastAPI. Implement semantic search, embeddings, vector databases, and advanced retrieval techniques. Build Text-to-SQL solutions and AI-powered database assistants. Integrate AI workflows with existing applications, databases, and third-party services. Improve AI accuracy, performance, reliability, security, and cost efficiency. Deploy and maintain AI applications in cloud or containerized environments. Required Technical Skills AI & Agent Engineering Strong hands-on experience with Python and LLM application development. Practical experience building RAG applications. Experience with LangChain and/or LangGraph. Experience building agentic AI workflows and multi-agent systems. Knowledge of prompt engineering, tool calling, structured outputs, and conversation state management. Experience integrating OpenAI, Claude, Gemini, or other LLM providers. Backend & Full Stack Python, FastAPI, REST APIs, and backend architecture. Frontend development using React, Angular, or similar frameworks. SQL databases such as PostgreSQL, SQL Server, or MySQL. Vector databases such as Qdrant, Chroma, Pinecone, Weaviate, or pgvector. Git, Docker, and application deployment. Nice to Have Experience with CrewAI or other multi-agent orchestration frameworks. Advanced RAG techniques, including hybrid search, reranking, query rewriting, and metadata filtering. Knowledge graphs and graph-based retrieval. AI-powered workflow automation and human-in-the-loop approvals. AI evaluation, observability, tracing, and prompt-injection defenses. Experience with .NET/C# and integration of AI solutions into enterprise applications. Experience building secure, multi-tenant SaaS applications. Ideal Candidate We are looking for someone who has built and deployed working AI applications, not just completed tutorials or experimented with basic chatbot examples. You should be able to make sound architectural decisions, write clean and maintainable code, troubleshoot complex problems, and take ownership of features from development through deployment. How to Apply Please submit your updated CV/resume in European format (Europass format preferred) along with your Upwork proposal. Your application should include: Your professional summary and relevant work experience. Technical skills in Python, RAG, LangChain, LangGraph, CrewAI, and full-stack development. Details of AI projects you have built and deployed. Links to your GitHub, portfolio, or live project demos. Your hourly rate and availability. Please highlight your hands-on experience building production-ready RAG applications, agentic AI workflows, and multi-agent systems. Applications without a CV/resume and relevant project experience may not be considered.