Build a Cloud-Native RAG-Powered Knowledge Assistant (Gen-AI, LangChain, Vertex AI/AWS Bedrock)

We're looking for a highly skilled Gen-AI Engineer to build a mini production-ready Knowledge Assistant using RAG (Retrieval-Augmented Generation) architecture. The assistant should ingest a small set of documents (PDFs or text), store them in a vector database, and allow query-based interactions using LLMs via Google Vertex AI / AWS Bedrock / Gemini API. This is a small project (10–20 hours) aimed at showcasing your ability to build intelligent, cloud-native Gen-AI applications. ______________________________________ Deliverables: •⁠ ⁠Set up cloud environment (GCP/AWS) •⁠ ⁠Ingest documents and build a vector store (e.g., FAISS, Pinecone, or similar) •⁠ ⁠Implement a basic RAG pipeline using LangChain or LangGraph •⁠ ⁠Expose the system via FastAPI (or Django minimal setup) •⁠ ⁠Deploy the application using Docker and provide a working CI/CD flow (GitHub Actions or similar) •⁠ ⁠Basic UI or API documentation for testing queries ______________________________________ Preferred Tech Stack: •⁠ ⁠LLM Platform: Google Vertex AI / AWS Bedrock / Gemini API •⁠ ⁠Framework: LangChain / LangGraph •⁠ ⁠Backend: FastAPI / Django •⁠ ⁠Vector Store: FAISS / ChromaDB / Pinecone •⁠ ⁠Infrastructure: GCP / AWS •⁠ ⁠Containerization: Docker •⁠ ⁠CI/CD: GitHub Actions / Cloud-native pipeline ______________________________________ Ideal For: •⁠ ⁠Gen-AI engineers seeking to build review-worthy, demonstrable projects •⁠ ⁠Professionals wanting to showcase LLM orchestration, RAG, and deployment skills •⁠ ⁠Developers experienced in AI automation and enterprise use-case alignment ______________________________________ Timeline: •⁠ ⁠Estimated Time: 10–20 hours •⁠ ⁠Delivery: Within 1 week preferred

Project ID: 39749553