AI/ML Engineer & Full-Stack Engineer with 5+ years designing and deploying production-grade AI systems, agentic workflows, and LLM-powered applications. Shipped products across HR tech, healthcare, and real estate — open to new opportunities and relocation.
name: "Hassan Naeem"
role: "AI/ML Engineer · Full-Stack Engineer"
location: "Michigan, USA 🇺🇸 (open to relocation)"
experience: 5+ years
education:
- M.S. Computer Science · Concordia University Saint Paul
- B.S. Computer Science · Sir Syed University of Eng. & Tech.
focus:
- Production LLM & agentic systems
- RAG pipelines & LLM orchestration
- Full-stack web & React Native mobile
verticals: [HR Tech, Healthcare, Real Estate, E-Commerce]
currently:
building: "Production AI agents & LLM-powered apps"
open_to: "AI/ML & Full-Stack roles — relocation OK"
ask_me_about: [LLMs, Agents, RAG, React Native, Python, TypeScript]I'm an AI/ML Engineer and Full-Stack Engineer with 5+ years of experience designing and deploying production-grade AI systems, agentic workflows, and LLM-powered applications at scale.
I've translated complex AI research into high-impact commercial products across HR tech, healthcare, real estate, and e-commerce, integrating large language models, machine learning pipelines, and real-time data architectures with React, Next.js, React Native, Python, and cloud infrastructure on AWS and GCP.
Currently open to AI/ML and Full-Stack roles, full-time, contract, or freelance. Open to relocation.
Architected and deployed a production AI Agent system integrating VAPI.ai LLM, HeyGen, AWS Transcribe, and OpenAI APIs, enabling voice-command interactions, autonomous content generation, and real-time dashboard control. Designed agentic recruitment workflows with LLM-driven candidate ranking, interview-scheduling automation, and offer-letter generation via BambooHR integration. Built a GraphQL campaign-intelligence module with ML-driven audience segmentation and shipped CI/CD on AWS with Docker + Kubernetes.
Led frontend of Take Charge EV-charging platform (React Native, TypeScript, Django, Firebase) with real-time driver tracking, Authorize.net payments, and 100MS audio calling. Built DateJar matchmaking app (React Native, GraphQL, Agora SDK) with audio/video calling and in-app purchases. Optimized Nouvoprix marketplace with advanced product search and real-time messaging.
Developed GroceryDone4u e-commerce platform (React Native, Node.js, TypeScript, Firebase, Stripe, Google Maps) with multi-store inventory, food ordering, and real-time delivery tracking. Optimized Pacsend — a peer-to-peer package delivery platform (Angular, Node.js, GraphQL, Stripe) with real-time tracking and payment infrastructure.
An advanced LLM intelligence layer combining RAG, multi-hop reasoning over knowledge graphs, continuous learning from user feedback, and parameter-efficient fine-tuning with LoRA / QLoRA. Multi-service architecture with a FastAPI backend and React frontend.
A Python agent that drives its own Chromium browser — no DOM shortcuts, no hidden state. Navigates nested iframes, does humanlike drag-and-drop on an HTML5 canvas, and reads the game board by sampling pixels. The same pattern generalizes to any canvas-based web app.
Production FastAPI service powering inbound carrier sales automation — load search, FMCSA MC-number verification, negotiation logging, and a webhook for the HappyRobot platform. Deployed on Fly.io with Docker + supervised processes and health-checked endpoints.
An interactive, gamified learning platform where beginners master programming through tutorials, quiz games, and hands-on coding sandboxes. XP, knowledge tree, achievements, and certificates across 43+ programming languages.
Architected an AI-driven property recommendation engine with Vertex AI and OpenAI for personalized user experiences. Built a hybrid React Native mobile app supporting immersive 3D property exploration and AR/VR hologram functionality for virtual furniture placement.
HIPAA-compliant healthcare platform with secure real-time patient-doctor communication via WebSockets and Firebase. Integrated Vertex AI and OpenAI to build an automated prescription-suggestion engine and predictive hospital-resource analytics.
AI-powered body measurement platform for e-commerce. Uses MediaPipe pose detection to extract 33 body landmarks from a photo, converts them to real-world measurements, and returns size recommendations against per-brand size charts.
Walking through the Python agent I shipped — nested iframe traversal, humanlike mouse drags, and reading a game board by sampling canvas pixels. The same pattern generalizes to Figma, Miro, and any legacy canvas-based internal tool.
A framework-free RAG you can actually read end-to-end — FAISS index, sentence-transformer embeddings, top-k retrieval, and an LLM call. No LangChain magic, just the four moving parts.
What an AI agent actually does when it "uses a tool" — the loop in ~100 lines. No framework, no hidden orchestration. Send tools, receive tool_use, run locally, send results, repeat.
Six chunking strategies — fixed, recursive, semantic, structural, parent-child, and contextual — with when to use each and the red flags to watch for. Everything I've learned building retrieval systems.
Twelve practical patterns for shipping with LLMs — role + task + constraints, few-shot that works, structured XML output, self-check pass, refuse-and-explain, and the anti-patterns to drop.
Score LLM generations against a reference with a strong model acting as judge. Rubric-based, returns structured JSON, aggregates correctness / completeness / clarity across a sample set.
The patterns I keep reusing — nested iframe traversal, global coordinate math, humanlike mouse drags, canvas screenshotting, and first-run browser install that self-heals. Starter kit for browser agents.
Concordia University Saint Paul
Sir Syed University of Engineering & Technology
I'm open to AI Engineer, Full-Stack, and Mobile opportunities, full-time, contract, or freelance. If you're shipping something interesting in GenAI, agentic systems, or product engineering, I'd love to hear from you.