End-to-End Generative AI, RAG & Multi-Agent Systems, Machine Learning and Analytics

About this Gig

I design and deploy production-grade Generative AI systems, specializing in RAG pipelines, multi-agent LangGraph orchestration, and end-to-end MLOps. As a hands-on builder, I do not just write scripts; I deliver fully deployed, self-driven systems. What I Offer: * Advanced RAG Pipelines: Custom document retrieval systems using ChromaDB, Pinecone, and semantic chunking. * Multi-Agent AI: Complex, deterministic agentic workflows using LangGraph and LangChain. * MLOps & Deployment: Full backend orchestration using FastAPI, Docker, MLflow, and secure deployments via Nginx and SystemD on DigitalOcean.

Requirements

To engineer the perfect AI solution for you, please provide the following details: Core Objective & Use Case: What specific problem are we solving? (e.g., automating customer support, internal document research, predictive maintenance). The Data Sources (Crucial for RAG): What data will the AI need to ingest? Please provide any relevant files (PDFs, CSVs), URLs, or database schemas. Infrastructure & Deployment: Do you have an existing cloud environment (like DigitalOcean or AWS), or will I be setting up the deployment from scratch? LLM & API Preferences: Do you have a preferred model (Gemini, OpenAI, open-source via Groq), or would you like me to select the most cost-effective architecture for your needs? Expected Output: How will users interact with this? (e.g., a Streamlit web dashboard, an integrated FastAPI backend, or a Telegram/chat interface).

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