AI Integration Specialist

I'm an AI integration engineer. I turn LLMs from "chatbots" into full-fledged product components.

What I can do:

• RAG systems: Pinecone, Milvus, Qdrant, PGVector, chunking, embeddings, hybrid search (keyword + semantic)
• LLM integration: OpenAI, Anthropic, Mistral, local models via Ollama/vLLM
• Agents and frameworks: LangChain, LlamaIndex, CrewAI, Function Calling
• Performance: reducing latency, token costs, semantic caching
• Quality evaluation: LLM-as-a-judge, RAGAS
• Automation: unstructured data pipelines, CI/CD integration in Python/Node.js

Example RAG architecture:

• Ingestion: documents → chunking → embeddings → vector store
• Retrieval: query → vectorization → search for similar chunks
• Generation: context + query → LLM → answer

Typical questions:

• "Add an AI assistant to a Telegram bot based on a knowledge base"
• "How do I cache OpenAI requests so I don't overpay"
• "Which vector DB should I pick for 10 million documents"

Describe the process you want to automate - let's break it down step by step.