Hello, I'm👋
Mohammad
Ali Nabil.
Software Developer & AI Systems Engineer
Computer Science undergraduate at North South University and former GenAI intern at The Data Island. I build production-grade software, autonomous multi-agent pipelines, RAG systems, and full-stack web applications with Python, FastAPI, and modern frontend tools.
const engineer = {
name: "Mohammad Ali Nabil",
role: "Software Developer & AI Builder",
location: "Dhaka, Bangladesh",
passion: "Building hardcore systems, not just calling APIs",
focus: [
"Autonomous Multi-Agent Systems (LangGraph)",
"LLM & Large Multimodal (LMM) Fine-Tuning & Model Training",
"Retrieval-Augmented Generation (Dense/Sparse RAG)",
"Scalable Full-Stack Web Backends (FastAPI, React)",
],
};
Execute Terminal.
Query the vector knowledge base directly or run live agent simulations through an interactive developer shell.
Interactive RAG Shell [Connected to Vector Store & Gemma LLM]
Type any technical question, run commands, or select presets above.
_ _ _ ___ ___ _ | \| |/_\ | _ ) _ _| | | .` / _ \ | _ \| || |__ |_|\_/_/ \_\___/___|____|
SYSTEM ARCHITECTURE
Autonomous RAG & Multi-Agent Runtime
OS / KERNEL:Agentic Linux x86_64 (FastAPI + Next.js)
VECTOR STORE:Pinecone Serverless (1024-dim llama-text-embed-v2)
INFERENCE LLM:Google Gemma-4-31B-IT via OpenRouter
AGENT WORKFLOW:LangGraph StateGraphs + MCP Tool Schema
SYSTEM STATUS:🟢 Online & Responding (< 350ms)
Experience & Education.
Applied engineering experience in GenAI workflows combined with rigorous foundational computer science training.
AI Engineer Intern
Engineered end-to-end AI automation workflows, high-precision structured data extraction pipelines, and agentic reasoning verification frameworks.
BSc in Computer Science & Engineering
Comprehensive computer science curriculum balancing rigorous algorithmic theory with modern applied machine intelligence and backend software engineering.
Featured Systems & Applications.
Production-grade agentic architectures, low-latency streaming pipelines, and full-stack software deployments.
FITMAN
Full-Stack AI Fitness & Nutrition Coaching Platform
Production-deployed fitness and nutrition platform orchestrating LangGraph multi-agent state machines, Groq streaming inference, and real-time FastAPI backends for personalized daily coaching and health telemetry adherence.
End-to-End Autonomous Agent
Self-Reflective Multi-Step Reasoning Engine
Autonomous agent framework equipped with persistent Pinecone vector memory, dynamic tool calling capabilities, web retrieval, and self-correcting evaluation loops.
AI Resume Analyzer
LLM-Powered Semantic Parsing & Job Fit Scoring
Intelligent resume parsing and ATS optimization engine using LLMs, vector embeddings, and semantic similarity scoring to evaluate candidate skills, match job descriptions, and deliver actionable CV insights.
Real-Time AI Voice Agent
Low-Latency Conversational Voice Pipeline
Full-duplex conversational voice agent integrating real-time streaming Speech-to-Text (STT), low-latency LLM reasoning, and natural neural Text-to-Speech (TTS) for human-like auditory interaction.
Bangla AI Voice Agent
Bengali Speech Recognition & Speech Synthesis
Native Bengali conversational AI agent supporting end-to-end Bangla Speech-to-Text, contextual LLM inference in Bengali, and low-latency Bangla neural speech generation for localized verbal assistance.
LiveKit MCP Voice Assistant
Bidirectional WebRTC Voice Assistant with MCP
Voice-first AI assistant utilizing LiveKit WebRTC for full-duplex speech communication, Model Context Protocol (MCP) server tools, and low-latency neural speech synthesis.
AI Vision & OCR Automation
Real-Time Video Stream & Document Extraction Engine
Computer vision automation pipeline leveraging OpenCV video filtering, Hugging Face vision transformers, and OCR models for real-time document extraction and object classification.
Technical Knowledge Graph.
Complete engineering hierarchy: from foundational Python runtime, asynchronous backends, and databases to tensor calculus, deep neural transformers, autonomous state graphs, and LLM/LMM fine-tuning.
Deep mastery of the Python runtime, metaprogramming, OOP design patterns, and package tooling.
Asynchronous microservices, high-concurrency API design, and network protocol routing.
ACID transactions, relational schemas, indexing strategies, and Supabase / PostgreSQL optimization.
Systems engineering best practices, Linux environments, testing suites, and clean architecture.
Mathematical foundations of machine learning, tensor algebra, probability, and classical modeling.
PyTorch tensor operations, backprop calculus, sequence models, multi-head attention, and LLM model training workflows.
Autonomous state graph agents, dense/sparse vector retrieval, LLM & LMM (multimodal) fine-tuning (LoRA/QLoRA), and model alignment.