Arifuzzaman J.

AI Integration | AI Agent Development, RAG, LangChain, Self-Hosted LLM

AI Integration and AI Agent Development. I build RAG systems, AI chatbots, and multi-agent workflows in Python, then cut the inference bill 60 to 80% by running them on self-hosted GPUs instead of managed APIs. Most AI projects do not fail at the demo. They fail at the invoice. Chunking is wrong, there is no evaluation, agents loop, and the API bill scales faster than the product. That gap is what I close. WHAT I BUILD AI Agent Development and Workflow Automation Multi-agent systems with tool calling, multi-step workflows, and human-in-the-loop approval. LangChain, LangGraph, and MCP servers. Agents that fail loudly and recover instead of looping forever. RAG and AI Chatbot Development Retrieval-augmented generation over PDFs, URLs, JSON, CSV, and internal wikis. Chunking strategy, embeddings, vector database setup (Pinecone, Qdrant, pgvector, ChromaDB), hybrid retrieval, reranking, and citations back to source. Every build ships with an evaluation harness, so retrieval quality is a number instead of an opinion. LLM Integration OpenAI, Anthropic Claude, Gemini, and open-weight models (Llama, Qwen, Mistral, DeepSeek) behind one interface. Python and FastAPI services, structured output with schema enforcement, streaming, retries, and per-request cost tracking. Fine-Tuning and Generative AI Modeling LoRA and QLoRA training on your data. Hot-swappable, domain-routed adapters served from a single base model, so one GPU covers many specialized behaviors. Deployment and MLOps vLLM serving, Docker, Kubernetes, CI/CD, monitoring. Serverless GPU endpoints on RunPod, Modal, and Vast.ai with zero idle cost. WHY THIS IS RARE Most AI developers can call an API. Far fewer can keep a self-hosted model running through driver updates, OOM, and multi-GPU placement. My infrastructure code is merged into a production open-source GPU deployment platform, public and reviewable before you hire me. I am also a published ML researcher, 7 peer-reviewed papers, 4 in Q1 journals. GOOD FIT IF - You need AI integration into an existing product or internal workflow - You have a RAG prototype with no evaluation and no idea if retrieval works - Your AI chatbot works in testing and breaks on real users - You are moving off OpenAI or Anthropic APIs to self-hosted open-source models - Your API or GPU spend is growing faster than your usage I will build a working proof of concept on your actual data before you commit to a full contract. My hours overlap US mornings and the full European business day. Send your use case, latency target, and monthly AI budget. I reply within 2 hours, and I will tell you if it is not achievable at that price before you spend anything.