Yaseen Nurmahammad Khatib is a Senior Full-Stack AI Engineer who builds and ships autonomous AI products — from Agentic RAG pipelines and LLM orchestration to the high-throughput MERN systems they run on. He turns AI capabilities into production software, at AI-speed.
Based in Hyderabad (IST) · Remote-first, effective across global time zones.
Shipped products and autonomous pipelines — built lean, deployed at AI-speed.
Ultra-lightweight streaming cockpit
A Rust-powered desktop cockpit for Twitch & YouTube streamers — live system telemetry, multi-platform chat velocity, and automated OBS scene sync, all inside a ~152 MB / 1.8% CPU footprint.
Autonomous content scheduler
A Python autonomous agent running on GitHub Actions that schedules and publishes technical content on a cron — drafting every post through the Gemini API. Zero servers, zero manual posting.
Git-triggered article factory
A native Next.js pipeline that autonomously writes, formats, and deploys Markdown articles straight to this site — Gemini drafts the MDX, GitHub Actions commits it, and GitHub Pages ships it.
Query my experience through a context-grounded RAG pipeline — live execution traces, structured outputs, and no answers outside the indexed corpus.
AI handles the syntax; the engineer dictates the flow. Master the pipeline: Database → Backend → Frontend. An AI-equipped architect ships the volume of an entire engineering squad. Hold the reactor to see the difference.
A Rust-powered desktop cockpit for streaming professionals, engineered via modular Claude orchestrations. Handles live system telemetry feeds, multi-platform chat velocity streams, and real-time automated OBS scene synchronization.
A Tier-1 support assistant for streamerOS built on a grounded RAG architecture. A Hono router on Cloudflare Workers embeds the product knowledge base into Upstash Vector, retrieves the relevant passages per question, and streams a gemini-flash answer constrained strictly to that context — refusing anything out of scope.
A self-hosted autonomous agent that lives entirely in a GitHub repository. On a cron schedule, a GitHub Actions runner drafts a technical post with the Gemini API, publishes it, and commits its state back to the repo — no server, no subscription.
A native Next.js pipeline that autonomously writes, formats, and deploys Markdown articles straight to this site. Gemini drafts the MDX, GitHub Actions commits it, and GitHub Pages ships the static export — at a steady-state cost of exactly $0.
An autonomous agentic retrieval-augmented generation engine deployed for law enforcement workflows. Scans massive multi-format legal structures to confidently trace, evaluate, and output systematic verdict matrices directly to investigators.
An interactive workflow-automation environment featuring responsive connectors, processing layers, and directional edge bindings. Developed a custom state Serialization Adapter architecture to optimize graph serialization over the wire.
A scaled administrative core engine supporting thousands of active endpoints. Hardened through structured cache rings and strict role-based authorization layers for low-latency, high-availability operation.
A high-throughput clinical workflow orchestration platform. Optimizes lookup routines through heavily cached queries and defensive server request-validation pipes for reliable patient-doctor data flow.
Embedded native smart-television systems engineered using modular vanilla abstractions for pristine execution over limited, multi-resolution, low-spec client platforms.
Structural system architectural optimization and deep defensive codebase translation for an enterprise core application. Implemented system type-safety definitions and refactored core data channels.
Jun 2025 — Present
Architecting autonomous Agentic workflows and integrating LLMs into production MERN systems — owning intelligent systems from design and orchestration through deployment.
Jan 2023 — May 2025
Sep 2021 — Jan 2023
A 15-part masterclass on building production-grade AI systems — from rethinking the MERN stack to hybrid retrieval, observability, FinOps, and stateful agents.
The AI-Native Dev Stack
Foundations
Beyond the Prompt
Foundations
Vector Foundations
Foundations
RAG: Grounding the Agent
Systems
Agentic Control Loops
Systems
Latency-First AI
Production
The Model Context Protocol
Production
94% Compression
Production
Guardrail Engineering
Production
Hybrid RAG
Production
LLM Observability
Production
FinOps for AI
Production
Evaluation-Driven Dev
Production
Memory & Stateful AI
Production
The AI-Native Portfolio
Career
A copilot completes your line; an agent executes your task. Why the difference is categorical — not incremental — and why it separates a 10% speedup from a 10x one.
Read Article →Rich UI objects make terrible database records. The Serialization Adapter pattern that separates render model from transport record — and cut IntegrateX payloads by 94%.
Read Article →Demo vector search is one query; production RAG is a pipeline. Chunking strategy, metadata-filtered hybrid search, and index freshness — the architecture behind the Police RAG system.
Read Article →