AI / ML Engineer

Mohammad Yaser
Hussain

I am Mohammad Yaser Hussain, a fresh AI and Machine Learning graduate from Jayaprakash Narayan College of Engineering, affiliated to JNTU, Hyderabad.

I work across the full range of AI engineering, from systems-level thinking and performance optimization to designing intelligent pipelines and applied machine learning. I am not attached to any one approach or tool. I follow the problem and figure out what it actually needs.

Building software that works in the real world matters to me more than building software that looks good in a demo. That means caring about performance, reliability and the experience of whoever ends up using it. I am also a published IEEE researcher with work in machine learning applied to real world market analysis.

Email Me GitHub LinkedIn +91 77299 12028
4
Projects
1
IEEE Paper
7.6
CGPA
May '26
Graduated
01 — Work

Project Experience

Project 01

Android · On-Device LLM

QuantLM: Native Android LLM Inference Engine

GitHub
  • Privacy-first on-device engine running Llama, Phi, and Qwen LLMs alongside Vision-Language Models entirely offline on 2 to 3 GB RAM hardware
  • Custom C++17 JNI bridge integrating llama.cpp, Google MediaPipe, and TensorFlow Lite for low-latency multimodal text and vision inference
  • Asynchronous model loading and memory management via Kotlin Coroutines, ensuring stable inference and a non-blocking UI under heavy computation
  • Clean Architecture, Dagger Hilt, and WorkManager powering background hot-swapping of multi-gigabyte model files
KotlinC++17 / JNIllama.cpp TensorFlow LiteMediaPipe Dagger HiltJetpack ComposeWorkManager

Project 02

Windows Desktop · Autonomous AI

F.R.I.D.A.Y: On-Device Multimodal AI with Autonomous System Control

GitHub
  • Fully offline Windows desktop AI assistant with Qwen2.5 LLM, multimodal vision, STT via faster-whisper, and TTS with zero cloud dependencies
  • Hybrid ML/DL intent pipeline combining LinearSVC and TF-IDF for near-instant classification with DistilBERT (ONNX) for complex queries, routed by complexity score with adaptive CPU/GPU scheduling
  • Concurrent PyQt6 application decoupling DL inference, wake-word detection via openWakeWord, and OS system control across isolated worker threads with structured JSONL telemetry
  • Semantic memory via ChromaDB and SQLite with spaCy NLP for entity extraction and multi-turn context, alongside real-time audio waveform and hardware telemetry panels
Python / PyQt6DistilBERT / ONNXChromaDB spaCyfaster-whisper openWakeWordQwen2.5SQLite

Project 03

Multi-Agent AI · Financial Analysis

SEC Insider Trading Analysis: Multi-Agent System

GitHub
  • Multi-agent orchestration via CrewAI Flow coordinating autonomous agents across real-time data fetching, SEC EDGAR filing analysis, and report generation
  • Real-time Form 4 and 8-K ingestion via ATOM feeds with Pydantic for type-safe config and BeautifulSoup for robust XML parsing
  • Data validation guardrails enforcing integrity, with Google Gemini 2.0 Flash auto-verifying and correcting generated insight reports against raw filing data
PythonCrewAI PydanticBeautifulSoup Gemini 2.0 FlashSEC EDGAR

Project 04

Amazon Hackathon 2025 · Multimodal Deep Learning

Multimodal Product Price Predictor

GitHub
  • Multimodal deep learning model predicting e-commerce product prices from text and product images, achieving 26.51% validation SMAPE across 75,000 products
  • Frozen MiniLM-L6-v2 text encoder (384 dims) fused with frozen MobileNetV2 image encoder (1280 dims) via a 3-layer MLP, keeping trainable parameters at 459K on a 4GB GPU
  • Diagnosed and resolved severe underfitting through log1p price scaling and post-hoc linear distribution alignment, lifting predictions from a narrow $4 to $30 band to mean $27.34
  • Full inference suite covering an interactive CLI, a Tkinter GUI with threaded model loading, and batch generation scoring 75,000 products in 5.5 minutes
PyTorchHuggingFace TransformersMobileNetV2 MiniLM-L6-v2SMAPE Loss Tkinter GUIAmazon Hackathon
26.51%
Val SMAPE
75,000
Predictions
48 min
Train Time
103 MB
Model Size
02 — Capabilities

Technical Skills

Languages

Python Kotlin C++ / C++17

AI / ML

PyTorch TensorFlow Lite HuggingFace Transformers ONNX / ONNXRuntime scikit-learn MediaPipe sentence-transformers

LLM & Agentic AI

llama-cpp-python CrewAI Google Gemini API Ollama ChromaDB On-device LLM (Qwen · Phi · Llama) MediaPipe LiteRT

Android & Mobile

Jetpack Compose Android NDK / CMake Kotlin Coroutines Dagger Hilt Room Database Vulkan GPU

NLP, Vision & Tools

spaCy faster-whisper OpenWakeWord OpenCV Pandas / NumPy Pydantic PyQt6 BeautifulSoup4
03 — Research

Publications

Peer-Reviewed · Journal Article · 2024

Enlightening Paths: Python's Vision into the Electric Vehicle Market

Hussain, M. Y. · Abrar, M. A. · Aiman, M. I. · Srinivas, T. A. S.

Journal of Communication Engineering & VLSI Design, Vol. 2(2), pp. 28–35

View on ResearchGate ↗
View Paper
04 — Background

Education & Certifications

Degree

Bachelor of Technology

Jayaprakash Narayan College of Engineering
Affiliated to JNTU, Hyderabad

AI & Machine Learning CGPA 7.6 / 10 Graduated May 2026

Certifications & Training

AI Internship

Edunet / IBM SkillsBuild

Nov–Dec 2024