LangChain & RAG Systems Developer
Production LLM applications — not just demos.
I build LLM applications that work in production — not just demos that hallucinate under real data. From RAG pipelines over your company knowledge base to multi-agent systems that automate complex workflows, I design, build, and deploy AI systems with proper evaluation, observability, and fallback handling.
What you get
What I Deliver
RAG pipelines
Document ingestion, chunking, embedding, retrieval, and LLM synthesis — built on your data. Pinecone, pgvector, Weaviate, or Chroma depending on your scale.
LangGraph multi-agent systems
Stateful AI agents that use tools, call APIs, and make decisions over multiple steps. Built with proper graph structure, human-in-the-loop checkpoints, and error recovery.
LLM integration into your product
Wrapping OpenAI, Claude, Mistral, or local models into your existing Node.js or Python backend with streaming, retries, and structured output.
Prompt engineering & optimization
System prompt design, few-shot examples, chain-of-thought structuring — and evaluation pipelines to measure if changes actually improve output quality.
LLM observability
Langfuse integration for tracing, latency tracking, cost monitoring, and prompt versioning. You see exactly what your AI is doing in production.
Fine-tuning with LoRA
Custom model fine-tuning on your domain data when prompt engineering isn't enough. Training, evaluation, and deployment of fine-tuned models.
Is this right for you?
Who I Work With
- SaaS products wanting to add AI features that actually work reliably
- Companies building internal knowledge base chatbots over proprietary documents
- Startups building AI agents that automate customer-facing or internal workflows
- Teams that built an LLM prototype and need it productionized properly
- Businesses evaluating OpenAI vs Claude vs open-source and needing guidance
Tech Stack
Real-world result
Seen in Production
Jaspi.io — AI Hiring Platform
Built AI screening pipelines that automate the full recruitment lifecycle at scale — job post generation, candidate sourcing, async video interviews, and real-time fit scoring. The system screens hundreds of applicants simultaneously and consistently, reducing time-to-hire from 30+ days to 48 hours.
Read the full case studyCommon Questions
What's the difference between LangChain and LangGraph?
LangChain is a framework for building LLM chains and RAG pipelines — great for document Q&A, structured extraction, and sequential workflows. LangGraph is built on top of it for stateful, multi-step agents where the AI needs to make decisions, use tools, and loop back. For complex agents, LangGraph; for RAG, LangChain is often enough.
How do you prevent hallucinations in RAG systems?
Several ways: better retrieval (reranking, hybrid search), source citation with grounding, confidence thresholds, output validation, and evaluation pipelines that catch regression. Hallucination is a retrieval problem as much as a model problem — better chunks and better context usually fix more than prompt tweaking.
OpenAI vs Claude — which should I use?
For most production RAG and agent work, Claude 3.5 Sonnet handles long contexts better and is more reliable on instruction-following. GPT-4o is strong for structured output and function calling. The right answer depends on your use case — I can run a comparison on your specific task.
Can you integrate LLMs into our existing Node.js backend?
Yes. I build the AI layer as a clean service that your existing backend calls — with streaming, structured output (JSON mode), retry logic, error handling, and cost tracking. No need to rewrite your backend.
Do you work with open-source models (Llama, Mistral)?
Yes. I deploy and integrate open-source models via Ollama, vLLM, or HuggingFace Inference Endpoints when data privacy or cost is a concern. I can also help you fine-tune on your domain data using LoRA.
Ready to get started?
I take 1–2 clients at a time to ensure quality. Get in touch and let's talk about your project.
Contact Me