👨‍💻 Mohid Naghman

AI/ML Engineer | Generative AI & Backend Developer

I'm an AI Engineer passionate about building production-grade systems that solve real problems. I specialize in generative AI, backend architecture, and deploying scalable systems that process real-time data efficiently.

Philosophy: Build working systems → Optimize ruthlessly → Scale intelligently

class MohidNaghman:
    def __init__(self):
        self.role = "AI/ML Engineer & Backend Developer"
        self.education = "B.S. AI & Data Science"
        self.location = "Lahore, Pakistan"

    def specializations(self):
        return {
            "generative_ai": ["LangChain", "LangGraph", "RAG Systems", "Multi-Agent AI"],
            "backend": ["FastAPI", "PostgreSQL", "Redis", "Event-Driven Architecture"],
            "ml_dl": ["PyTorch", "TensorFlow", "CNN", "Transformers"],
            "devops": ["Docker", "CI/CD", "Production Deployment"]
        }

    def current_focus(self):
        return [
            "Building production-grade AI applications",
            "Optimizing LLM performance and cost",
            "Scalable backend architectures",
            "Real-time AI systems"
        ]

    def contact(self):
        return {
            "linkedin": "linkedin.com/in/mohid-naghman/",
            "email": "mohidnaghman0@gmail.com",
            "github": "github.com/MohidNaghman1"
        }

🧠 Generative AI & Machine Learning

Layer	Technologies
GenAI Frameworks
LLM Providers
Vector DBs & Search
Deep Learning
NLP & ML
Advanced Techniques	Prompt Engineering · Few-Shot Learning · Chain-of-Thought · Retrieval Augmentation · Multi-Agent Orchestration · Function Calling

🎯 AI Specializations

• Multi-Agent Systems: Autonomous agents, hierarchical workflows, tool use & function calling
• RAG Pipelines: Document chunking, semantic search, context optimization, retrieval strategies
• Prompt Engineering: Advanced prompting patterns, role-based prompts, structured outputs
• LLM Optimization: Cost reduction, latency optimization, token management, streaming responses

⚙️ Backend, API & Infrastructure

Layer	Technologies
Languages	(Advanced OOP · Async) · ·
Web Frameworks
Databases
Caching & Messaging	[Message Queue Patterns]
Real-Time
Async & Concurrency	Async/Await · asyncio · Event Loop Management · Coroutines · Background Tasks
Auth & Security

🏗️ Architecture Patterns

• Microservices: Event-driven, asynchronous, loosely coupled
• CQRS: Command Query Responsibility Segregation
• Message Queues: Producer-Consumer, PubSub, Work Queues
• Background Jobs: Celery tasks, scheduled jobs, worker pools

🚀 DevOps, Cloud & Deployment

Aspect	Technologies
Containerization
CI/CD Pipelines
Cloud Platforms
Monitoring & Logging	Application Performance Monitoring (APM) · Structured Logging · Error Tracking · Health Checks
Database Migrations	· Schema versioning · Zero-downtime migrations

💻 Full-Stack Development

Aspect	Technologies
Frontend Frameworks
Styling & UI
State Management	React Context · Zustand · Redux · Jotai
Testing

🧠 AI-powered crypto intelligence platform with real-time distributed event processing

An enterprise-grade, event-driven crypto intelligence system that ingests high-velocity signals from multiple sources (RSS feeds, market APIs, Ethereum blockchain streams), intelligently scores them with LLMs, filters noise with ML models, and delivers real-time alerts across email, Telegram, and WebSocket channels with 99.9% uptime.

Microservices via RabbitMQ · Worker pools with Celery · Event sourcing with Redis · PostgreSQL event log

Backend: FastAPI · Uvicorn · Python 3.11+
Database: PostgreSQL 16 · SQLAlchemy ORM · Alembic migrations
Messaging: RabbitMQ · Celery (task queue) · Redis (cache/session)
Real-Time: WebSocket · Server-Sent Events · Telegram Bot API
Deployment: Docker · Docker Compose · Render · GitHub Actions CI/CD
Data Sources: RSS (feedparser) · REST APIs · Web3.py (blockchain)

• Signal Processing Latency: <500ms end-to-end
• Throughput: 10K+ events/second processing capacity
• Availability: 99.9% uptime target
• Alert Delivery: <2 seconds mean latency

🌐 Full-stack production content generation & summarization platform powered by Google Gemini

A production-ready AI ecosystem featuring enterprise authentication, streaming AI conversations, intelligent content summarization, and a Redis-backed event notification system — fully containerized, tested, and deployed with zero-downtime updates.

Backend: FastAPI · Pydantic models · Async handlers
Database: PostgreSQL · SQLAlchemy ORM · Alembic versions
AI/ML: LangChain · Google Gemini API · Prompt templates
Infrastructure: Redis (cache/notifications) · RabbitMQ queues
Deployment: Docker · Docker Compose · Render · GitHub Actions

• API Response Time: <200ms (p95)
• LLM Generation Speed: 40+ tokens/second
• Concurrent Users: 500+ supported
• Content Generation Accuracy: 94% user satisfaction

🎯 Multi-agent AI system orchestrated with LangGraph delivering personalized career advisory

A sophisticated multi-agent platform leveraging LangGraph orchestration to coordinate 5 domain-specialized AI agents with real-time web search integration — achieving <2 second end-to-end streaming response latency.

AI/ML: LangGraph (multi-agent orchestration) · LangChain · Groq LLaMA-3
Backend: FastAPI · Async endpoints · SSE streaming
Frontend: Next.js 14 · React 18 · Tailwind CSS
Data: FAISS (vector store) · Semantic search · Web search APIs
Deployment: FastAPI on Railway · Frontend on Vercel

📖 Production RAG system providing semantic access to unstructured university ERP records

A sophisticated Information Retrieval system that autonomously scrapes, chunks, embeds, and indexes unstructured university ERP data — enabling students to query complex policies using natural language with 92% accuracy.

• Web Scraping: Automated university portal data collection via Selenium
• Document Processing: Multi-format extraction (PDF, HTML, TXT)
• Vector Search: FAISS semantic indexing with top-k retrieval
• Context Grounding: LLM responses with source citations
• User Interface: Real-time Streamlit query interface

Data Ingestion: Selenium (web scraping) · PyPDF2 · BeautifulSoup
NLP & Embeddings: LangChain · Groq LLaMA-3 · Sentence Transformers
Vector DB: FAISS (fast similarity search)
Frontend: Streamlit · Real-time query interface
Deployment: Streamlit Cloud · Async background jobs

• Query Accuracy: 92% precision on unstructured queries
• Response Latency: <3 seconds average
• Knowledge Base: 500+ indexed document chunks

B.S. Artificial Intelligence & Data Science — Superior University, Lahore

• Graduation: Expected May 2027 | GPA: 3.83/4.00
• Focus: Generative AI, Deep Learning, Distributed Systems, Production ML

• Multi-agent orchestration · LLM fine-tuning · RAG systems · Prompt engineering
• Cost/latency optimization · Streaming responses · Token management

• Event-driven microservices · Message queues (RabbitMQ, Celery) · Real-time systems
• PostgreSQL optimization · Cache strategies · Async programming (asyncio)

• Docker & Docker Compose · GitHub Actions CI/CD · Render, Railway, Vercel deployment
• Alembic migrations · Monitoring & logging · Infrastructure as code

• Next.js · React · Tailwind CSS · FastAPI · Testing & performance optimization

• Advanced multi-agent systems with LangGraph
• Production LLM latency & cost optimization
• Fault-tolerant event-driven architectures
• Scalable GenAI applications at enterprise scale

Ship working systems → Optimize ruthlessly → Scale intelligently

Solve real problems with proven technology. Measure before optimizing. Write code others can understand.

I'm interested in connecting with fellow engineers, AI enthusiasts, and businesses building intelligent systems.

📊 Profile Stats

⚡ Let's build something amazing together!

Currently exploring: Distributed AI systems · Advanced LLM applications · Open source AI tools