Achhar S.

AI Engineer specializing in AI Agents, RAG, LangGraph, LangChain, LLMs, and Python. I build AI systems that go beyond a proof of concept - AI agents, RAG applications, LLM-powered products, and automation workflows that connect with real data, APIs, tools, and existing software. The interesting part of AI development isn't getting an LLM to produce an answer. It's making the whole system work reliably: giving it the right context, connecting it to the right tools, handling edge cases, and integrating it into the product or workflow your business already uses. That's the kind of work I enjoy. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ◆ AI AGENTS & MULTI-AGENT SYSTEMS ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ • AI agents and multi-agent systems • LangGraph and Langroid • LangChain and CrewAI • Agent orchestration and tool calling • AI workflow and decision-making logic • LLM-powered task automation I recently worked on a technically challenging healthcare AI infrastructure project involving LangGraph/Langroid, LLM integration, RAG, LaTeX processing, and vector databases. The project was completed with a 5.0 rating, with the client specifically recognizing my technical contribution and professionalism. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ◆ RAG, VECTOR SEARCH & KNOWLEDGE SYSTEMS ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ • Retrieval-Augmented Generation (RAG) • Vector databases and semantic search • Pinecone and ChromaDB • Embeddings and document retrieval • Knowledge bases and context retrieval • Grounded LLM responses I focus on making retrieval useful in the actual application - not simply connecting a vector database and calling it RAG. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ◆ LLM APPLICATIONS & AI INTEGRATION ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ • OpenAI and Claude integrations • LLM application development • Prompt engineering • Structured outputs and tool/function calling • AI chatbots and intelligent assistants • Connecting LLMs with APIs, databases, and existing applications ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ◆ AI AUTOMATION & BACKEND ENGINEERING ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ • Python automation and AI tooling • n8n workflow automation • FastAPI and REST APIs • Node.js backend services • Laravel and PHP • Database and third-party API integrations • Scheduling, availability, and business workflow systems This combination of AI and full-stack experience is useful when the AI isn't a standalone experiment and needs to become part of a real product. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ◆ RECENT CLIENT WORK ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ • Senior Multi-Agent AI Engineering LangGraph/Langroid + LLMs + RAG + vector databases for a healthcare AI infrastructure project. Completed with a 5.0 client rating. • AI Development & Full-Stack Engineering 249 hours of AI and full-stack development work, completed with a 5.0 rating. The client highlighted my reliability, communication, collaboration, and ability to ask the right questions early. • Laravel Scheduling & Availability Built a scheduling and free/busy availability service from the ground up. Completed with a 5.0 client rating. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ◆ HOW I APPROACH AI PROJECTS ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ I don't start with a framework and try to force the problem into it. First, I understand what the system needs to accomplish, what information it needs access to, how users will interact with it, and where reliability could become an issue. Then I choose the architecture and tools around those requirements. Sometimes the right solution is a multi-agent LangGraph system. Sometimes a well-designed RAG pipeline is enough. And sometimes the best solution is a simple automation workflow without unnecessary AI complexity. The goal is not to use more AI. The goal is to build the right system. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ◆ WHAT YOU CAN EXPECT ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ • Clear communication and proactive updates • Practical technical recommendations • Clean and maintainable implementation • AI systems designed around real product requirements • Careful handling of edge cases and failure points • Integration with existing APIs, databases, and backend systems • A developer who asks questions before making expensive assumptions ************************************* If you're building an AI agent, RAG system, LLM application, AI chatbot, or AI automation workflow, send me the details. Tell me what you're building, what's currently not working, or what you want the system to do. I'll help you figure out the right technical approach and the next practical step.