Hire the Best LangGraph Specialists

I build production AI agents and the backends that run them LangGraph multi-agent systems and scalable APIs in NodeJS, NestJS & Django. Every project delivered at 5 stars. Most backend developers can wire up CRUD and call an LLM API. Fewer can design agentic systems that hold up in production with real state management, tool use, human-in-the-loop fallbacks, and clean architecture underneath. That’s where I focus. 🤖 AI & Agent Engineering • LangGraph multi-agent systems state machines, branching, human-in-the-loop • LangChain workflows & RAG pipelines • CRM and business-process automation powered by AI agents • Agents wired into real production backends, not just demos ⚙️ Backend Engineering • REST APIs clean, modular, scalable architecture • Auth & role-based authorization (JWT, OAuth, Kinde) • Database design & query optimization (PostgreSQL, MongoDB, MySQL) • Redis caching, WebSockets / Socket IO, real-time systems • Payment integrations (Stripe, Paystack, Flutterwave) ⭐ Recent work • Built and shipped a LangGraph agent system rated 5 stars. Client: “delivered beyond expectation… broke down the tasks, executed to timeline, delivered an excellent solution.” • Architected a custom CRM for a LangGraph product (NestJS, Redis, WebSockets / Socket IO) also rated 5 stars. 🧠 Stack TypeScript · JavaScript · Python | NodeJS · NestJS · Express · Django | LangGraph · LangChain | PostgreSQL · MongoDB · MySQL | Redis · Docker If you’re building an AI agent, an automation system, or a backend that has to handle real business logic not just basic CRUD let’s talk. I reply within a few hours and deliver production-ready work.

𝐈 𝐛𝐮𝐢𝐥𝐝 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐚𝐧𝐝 𝐑𝐀𝐆 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐭𝐡𝐚𝐭 𝐠𝐞𝐭 𝐦𝐞𝐚𝐬𝐮𝐫𝐞𝐝 𝐛𝐞𝐟𝐨𝐫𝐞 𝐭𝐡𝐞𝐲 𝐬𝐡𝐢𝐩, 𝐧𝐨𝐭 𝐚𝐟𝐭𝐞𝐫 𝐭𝐡𝐞𝐲 𝐛𝐫𝐞𝐚𝐤. Last RAG rebuild went from 18% to 87% Hit Rate@5 and now serves 1M queries a month at $400-600 all-in. LangGraph, Python, FastAPI, AWS. Your agent worked in the demo. Three weeks into production nobody can explain why it gave a wrong answer, what it cost to run last week, or whether it's getting worse. That's what happens when evals, observability, and cost controls get added after launch instead of before. 𝐖𝐎𝐑𝐊 𝐈'𝐕𝐄 𝐒𝐇𝐈𝐏𝐏𝐄𝐃 ▸ Maintenance triage agent (LangGraph, multi-tenant) 2-4 hours of manual review per request, down to under 30 minutes. Agent now resolves 60-85% end to end with zero human touch. Manager capacity doubled, no new hires. Shipped with a 14-grader eval harness and CI regression gates. ▸ Support RAG rebuild Hit Rate@5 18% → 87%. MRR@5 0.31 → 0.73. P95 time-to-first-token 3.2s → 1.9s with Redis semantic caching. Cost per correct answer dropped 15x. Root cause was a vocabulary gap between how customers ask and how the KB was indexed. Fixed at the source, not with a bigger model. ▸ Multi-tenant AI helpdesk One system, many client companies, zero data bleed. Per-tenant vector namespacing, token budgets, tool permissions scoped to each company's policy. Six adversarial attack tests run in CI and all must fail before code merges. Isolation enforced by infrastructure, not filtered by prompt. 𝐖𝐇𝐀𝐓 𝐈 𝐁𝐔𝐈𝐋𝐃 1. AI Agents & Multi-Agent Systems LangGraph agents, multi-step tool calling, MCP servers, human-in-the-loop approval gates, durable execution with Temporal so a crashed agent resumes instead of re-running and paying twice. 2. RAG & Retrieval Pipelines Hybrid retrieval, reranking, chunking strategy, vector DB selection, semantic caching, streaming. Retrieval that finds the right context consistently, and you have the numbers to prove it. 3. LLM Evaluation & Observability Eval harness built before the agent ships. Deterministic graders plus calibrated LLM-as-judge (took one from TPR 90%/TNR 53% to 95%/93%). Langfuse or LangSmith traces on every run. Deploys gated by evals, not by intuition. 4. Guardrails & Production Hardening Deterministic checks around every model call. PII redaction, RBAC, token budgets, cost caps, adversarial testing. Built for teams working under EU AI Act or similar. 5. Backend & Cloud FastAPI on AWS ECS, Lambda, Bedrock. Postgres with row-level security, Docker, CI/CD. Seven years of backend and cloud engineering before I moved to AI. 𝐒𝐓𝐀𝐂𝐊 Agents: Python · LangGraph · LangChain · OpenAI Agents SDK · Claude Agent SDK · Pydantic AI · AWS Strands · Temporal · MCP · A2A RAG: Qdrant · Pinecone · pgvector · Redis · Haystack · hybrid search · reranking · semantic caching Evals: RAGAS · DeepEval · Langfuse · LangSmith · LLM-as-Judge · pytest · GitHub Actions Infra: FastAPI · AWS ECS · Lambda · Bedrock · Docker · PostgreSQL · Terraform · LiteLLM Frontend: React · Next.js · TypeScript AWS Community Builder · AWS Agentic AI Certified · 7+ years cloud and backend before AI 𝐇𝐎𝐖 𝐈 𝐖𝐎𝐑𝐊 Most AI requests arrive already solved. "We need a RAG chatbot." "We need an agent for support tickets." I don't start there. I start one level up: what business outcome breaks if this doesn't exist, who feels it, and what does a correct result actually look like. Then I decompose backward into the system. That reordering is usually where the money is. The support RAG rebuild above was scoped as a model upgrade. The actual failure was a vocabulary gap between how customers phrase questions and how the KB was indexed. A bigger model would have cost more and fixed nothing. 𝐖𝐇𝐀𝐓 𝐇𝐀𝐏𝐏𝐄𝐍𝐒 𝐍𝐄𝐗𝐓 1. You describe the workflow that's stuck, not the solution you have in mind 2. Within 24 hours you get the problem decomposed: what actually breaks, what success would have to measure, and what one resolved task will cost at your volume 3. If it's worth building, we scope a first milestone with a defined eval gate Step 2 costs you nothing and is useful whether or not you hire me.

Full-stack AI engineer: I build autonomous AI agents, multi-agent systems, and production RAG into scalable SaaS products, end to end in Next.js and TypeScript. Recent work: 1,145 hours on a Claude API + Next.js platform and ~800 hours on a LangGraph product (Top Rated Plus, 100% 5-star feedback, $100K+ earned). "Ahmed is the person you want owning the surface where your product meets the user. He has a sharp UI mind and pairs it with unusual rigor about how things actually work underneath. Give him a vague problem and he returns a scoped plan with the trade-offs named and a recommendation attached." — Client review, AI platform contract (5 stars) FEATURED PRODUCTION CASE STUDIES • Starling MX — Enterprise AI Knowledge Platform (Next.js 15, Supabase RLS, Claude & OpenAI APIs, Notion API): 1,145 hours, 5 stars. Built durable background document conversion (PDF/DOCX/PPTX), prompt caching, context-window budgeting, streaming AI chat, and multi-tenant workspace architecture. • Gotobeat — Live Music Tech & Analytics SaaS: Full-stack Next.js/TypeScript architecture, automated reporting pipelines, live listener conversion analysis, and high-tempo product iteration. • Yazr AI — Autonomous Agent Workflows: Designed multi-agent execution graphs using LangGraph, structured tool/function calling, persistent state management, and real-time voice agent pipelines (LiveKit, ElevenLabs). WHERE I HELP FOUNDERS & TEAMS MOST • Moving from AI Demo to Production SaaS: Multi-tenant auth, streaming responses, cost guardrails, latency reduction, and UI states your users trust. • Agentic Workflows & Multi-Agent Systems: LangGraph, CrewAI, MCP (Model Context Protocol), and custom autonomous agent loops with persistent memory. • Grounded RAG & Document Intelligence: Hybrid semantic search, chunking strategies, vector databases (Pinecone, Qdrant), and document parsing pipelines. • Existing SaaS Integration: Embedding AI copilots, custom editors (TipTap), or automation engines into established codebases without disruptive rewrites. CORE TECH STACK • AI & Agents: Claude API, OpenAI API, Gemini · LangGraph, LangChain, MCP (Model Context Protocol) · RAG with Pinecone, Qdrant, ChromaDB · Function Calling, Structured Outputs · Voice Agents (LiveKit, ElevenLabs, Deepgram) • Product & Full-Stack: Next.js (App Router), React, TypeScript, Node.js, Python (FastAPI), NestJS, PostgreSQL, Supabase, Redis, Tailwind CSS • Cloud & Infra: AWS (Lambda, S3, EC2), Vercel, Docker, GitHub Actions, CI/CD pipelines HOW I WORK I start with a short, clear scoping plan (architecture, edge cases, trade-offs, and what to ship first), then build in rapid, testable increments you can review live as we go. Have an AI feature or product you need built or taken to production? Send me a quick description of what you're trying to solve, and I'll reply with how I'd architect it.

I build LLM agent systems that hold up in production — LangGraph orchestration, RAG over messy real-world data, and the evaluation layers that keep them reliable. Recent work: a multi-agent platform managing 13 interdependent workstreams with automatic dependency invalidation and a reviewer agent gating output; a retrieval layer combining keyword and semantic search with cross-encoder reranking and MCP tool calling against live APIs; a LangGraph conversational agent with deterministic guardrails living outside the model and an adversarial evaluation harness; a healthcare claims pipeline combining deterministic rules with fine-tuned models under HIPAA, where auditability decided the architecture. Also a full-duplex voice assistant over an ERP backend — streaming ASR, semantic endpointing, barge-in, WebRTC. Most of my time goes to routing, evaluation and failure handling rather than model calls. Integrating an LLM API takes a week; making it reliable enough that someone acts on the output is the job. Stack: Python, LangGraph, FastAPI, PostgreSQL + pgvector, Docker, Anthropic/OpenAI/Gemini, self-hosted inference via vLLM and Ollama. 7+ years, MSc in Artificial Intelligence, two published papers (ACL, SemEval).