AI Engineer

building and iterating on an internal platform and demo toolchain focused on RAG/LLM solutions

• developing python services using FastAPI and asynchronous processing patterns
• implementing hybrid retrieval combining vector search and BM25 using Azure AI Search and/or Qdrant
• designing document ingestion pipelines including PDF extraction, OCR, chunking and metadata filtering
• generating structured outputs such as Excel and Word documents for non-technical stakeholders
• maintaining and integrating Node.js and React modules connecting frontend with Python APIs
• developing end-to-end RAG pipelines from ingestion to structured output generation
• implementing event-driven workflows using messaging patterns and tools like RabbitMQ or Azure Service Bus
• building internal demo tools using Streamlit with multi-page apps and interactive components
• ensuring engineering quality through testing, Dockerization, reproducibility and CI/CD practices

I am looking for an experienced AI Engineer with strong Python and RAG/LLM expertise to join a fast-moving project focused on building an internal platform and demo toolchain. You will design end-to-end pipelines, work with hybrid retrieval, and deliver structured outputs for non-technical users. The role requires hands-on experience with FastAPI, async processing, and modern LLM workflows, along with the ability to collaborate across teams and iterate quickly in a dynamic environment., Python 3.12+ with strong OOP and clean architecture principles

• experience with FastAPI and backend API design
• asynchronous processing using Celery and messaging/event-driven systems
• experience with PostgreSQL and relational database design
• knowledge of RAG architecture including chunking, embeddings, retrieval and generation
• prompt engineering with versioning and structured output parsing
• experience with OpenAI API and embedding models
• hybrid retrieval approach combining vector search and BM25
• experience with Qdrant or similar vector databases including metadata filtering
• integration with Azure AI Search / Azure Cognitive Search
• PDF extraction using PyMuPDF (fitz)
• OCR processing using Tesseract/pytesseract
• advanced chunking strategies for legal or financial documents
• experience with Streamlit including session state and interactive components
• data processing using pandas, NumPy and Excel generation with openpyxl
• basic to intermediate knowledge of Node.js and React for integration tasks
• experience with Docker and Docker Compose
• CLI scripting and orchestration using argparse and bash
• strong communication skills and ability to collaborate with non-technical stakeholders
• ability to debug end-to-end pipelines independently

Nice to have

• experience with RAG evaluation frameworks and quality measurement
• knowledge of MCP or FastMCP
• experience with LangChain, LangGraph or multi-agent frameworks such as CrewAI, AutoGen, TaskWeave
• experience with Azure CI/CD pipelines and cloud-native deployments