Moiz
Baloch
A Computer Science undergraduate building machine learning, deep learning, and LLM systems, from experimentation to production. Fine tuning models, serving them, and engineering the infrastructure around them.
From Flutter apps to intelligent systems.
I started building production mobile and full-stack applications with Flutter, Node.js, and Firebase. That engineering foundation taught me how to ship real products. It became the base for the next step of the stack.
My work now centers on machine learning, deep learning, and LLM engineering: fine tuning models with QLoRA, building computer vision systems, serving them through FastAPI, and engineering the deployment layer around them.
The strongest thread across my work is systems thinking: moving from an idea to a client, a backend, a model, an API, and a deployment strategy. I build complete AI systems, not isolated notebooks.
Application engineering
Flutter · Dart · Firebase
Full stack systems
Node.js · MongoDB · Socket.IO
ML engineering
XGBoost · LightGBM · SHAP · Optuna
Deep learning & vision
PyTorch · YOLO · OpenCV · ByteTrack
LLM engineering
Transformers · LoRA · QLoRA · TRL
AI systems & serving
FastAPI · Docker · MLflow
Model internals
AtlasLLM · AtlasMoE · inference optimization
Before the models, there were apps.
I built a lot of them.
I didn't start in machine learning. I started by shipping apps. My first serious engineering era was mobile development with Flutter, and I treated it the way I treat models now: build it, ship it, make it real.
Full applications, not screens. Clients in Flutter, backends in Node.js, MongoDB databases, real time chat over Socket.IO, Firebase for auth and sync, and admin panels to run it all. The same product shaped for phones, tablets, and the people using them.
That era is why AI never felt like a jump. When I moved into machine learning, I already understood the whole product around the model: how a user touches it, how data flows, how a backend serves it. The intelligence layer just became the next thing to build.
AL-Safeena
Service marketplace and FYP. Flutter client, Node.js backend, real time chat, MongoDB, React admin panel, ML assisted provider ratings.
ShopEase
Ecommerce application with a complete shopping flow.
Synk
Chat application built on real time messaging.
iWENT
Event oriented application.
QuickPDF
PDF utility application.
BLYND
AI oriented social application concept.
UXelerate
UI and UX focused project work.
Khushi Kids
Children's story and educational app, designed in Figma.
Trade With Shaw
Built during the software development phase.
IdeaCatalyst
Web project built for Air University.
ten builds across the era, each with its own stack and its own problems
The stack I build with, end to end.
From model training and fine tuning to serving, deployment, and the infrastructure that keeps AI systems running.
ML frameworks
//Deep learning & vision
>>NLP / LLMs
~MLOps & serving
$Languages & tools
>Currently exploring
?Projects I've built & shipped.
Model fine tuning, computer vision, and AI systems, each with the engineering detail behind it. No screenshots, just the work.
Clinical Digital Twin
2024/26AI-powered personalized patient simulator trained on 534k+ MIMIC-IV hospital admissions. Five calibrated risk models predict mortality, readmission, ICU admission, length of stay, and clinical deterioration from the first 24 hours, with a grounded RAG assistant that refuses rather than fabricates.
VisionGuard
2026Commercial-grade computer vision platform analyzing live webcam, USB, RTSP, and IP streams in real time, with YOLO11 detection, ByteTrack tracking, and an event engine with nine rules served through a FastAPI + WebSocket API.
ResearchMate
2026A 3B instruction model fine tuned with LoRA on PubMedQA/arXiv so it can answer research questions from scientific literature. Evaluated on latency, quality, and hallucination behavior.
Math Tutor
2026Qwen2.5-1.5B-Instruct fine tuned on GSM8K with QLoRA to solve math problems step by step. Trained on a Kaggle T4 with 4-bit quantization and published to Hugging Face.
RBC Anomaly Detection
2024/25End to end medical diagnostic pipeline detecting red blood cell anomalies with XGBoost (~92%+ validated accuracy) and SHAP explainability, plus LLM generated clinical reports with a rule based fallback for zero downtime.
VisionForge
2026Real time, gesture controlled augmented reality effects. Hand and pose tracking drive visual overlays on live camera input.
J.A.R.V.I.S
2026Local first, privacy focused voice assistant: Whisper speech-to-text, Qwen2.5 reasoning, 40+ tool actions, and persistent memory for hands-free automation of repetitive tasks.
Echo-ProjectX
2026Offline capable, file system based automation assistant. The engineering foundation for a J.A.R.V.I.S style agent that runs without the cloud.
BlackHoleLab
2026A C++ computational physics simulator exploring black hole dynamics, particle trajectories, photon paths, gravitational lensing, and relativistic effects.
What's next, in the lab.
Forward looking builds I'm working toward. These are honest statuses: planned and in progress, not claimed as shipped.
AtlasLLM
A dense transformer LLM built from scratch in PyTorch, focused on understanding and implementing transformer internals rather than only fine tuning an existing model.
AtlasMoE
The successor to AtlasLLM: a Mixture of Experts LLM built from scratch, exploring sparse routing, distributed concepts, and efficient optimization.
F.R.I.D.A.Y
The next generation voice assistant evolving from J.A.R.V.I.S, with deeper tool use, richer memory, and fully local first operation.
NeuroScope
A neural network and ML reverse engineering visualization platform. A serious desktop project for exploring models from the inside.
PhysicsLab AI
A scientific simulation platform combining physics, equations, and simulations, connecting AI engineering with scientific computing.
> The pattern is deliberate. I keep moving down the stack: from apps to APIs, models, and now model internals.
builder mindset, alwaysWhere I've shipped.
Brain Hub Technologies (Trade With Shaw)
AI/ML & Mobile Developer Intern
Multan, Pakistan
- >As the only AI/ML developer on the team, designed and deployed stock price prediction models using Python based ML pipelines, contributing to a ~30% increase in trading revenue across internal operations and international client portfolios.
- >Built end to end pipelines from feature engineering to model serving, ensuring consistent, measurable performance across multiple client portfolios.
Al-Safeena
Senior Mobile Application Developer
Saudi Arabia · Remote
- >Built a home services freelance marketplace giving Saudi vendors a centralized platform for consistent business, built with Flutter and Node.js backends, contributing to a ~40% increase in company revenue.
- >Continuing as final year project: managing incremental feature development and deployment of the live platform.
Where I'm learning.
B.S. Computer Science
Air University, Multan Campus
Oct 2023 to Jun 2027
Final year project: Al-Safeena, the live home services marketplace built for a Saudi Arabian client.
Relevant coursework
- Machine Learning
- Deep Learning
- Data Structures & Algorithms
- Databases
- Mobile Application Development
Formally trained.
GIKI Advance AI Bootcamp
Ghulam Ishaq Khan Institute (GIKI)
Intensive bootcamp covering modern AI and ML engineering practice, from training to deployment.
Anthropic Claude 101 Courses
Anthropic
Hands on LLM engineering foundations, including Claude Code and model best practices.
Research, peer reviewed.
Adoption Readiness and Perceived Reliability of Generative AI Tools in Software Development Education
A. Bilal, E. Qazi, A. Noor, A. Moiz, M.A. Lodhi
Qualitative Research Journal for Social Studies (HEC Recognized) · 3(2), pp. 293 to 320
Let's build something intelligent.
Open to AI/ML engineering roles, research collaboration, and interesting projects. If you're building something in the ML or LLM space, I'd like to hear about it.
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