"Building AI systems that don't just think—they collaborate, create, and solve."
I'm a B.Tech Computer Science graduate specializing in Artificial Intelligence & Machine Learning from BATU University (DIEMS). With deep expertise in Generative AI, Multi-Agent Systems, and Full-Stack Development, I architect intelligent solutions that bridge cutting-edge research with production-ready applications.
🎯 Currently: Open to AI Engineering roles, Freelance Projects, and Startup Collaborations
🏆 Achievement: Won Smart India Hackathon (College Level) with an AI-powered Legal Assistant System
🧠 Thesis: Built a Multi-Agent AI System using LLMs & RAG for workflow automation
🌱 Learning: Advanced MLOps, Agentic RAG, and Production AI Systems
💡 Passion: Transforming complex AI research into scalable, real-world products
🌊 1. Diffusion Models & Generative AI
- ✅ Diffusion Model Foundations: Understanding DDPM, DDIM, and score-based models
- ✅ Stable Diffusion Implementation: Custom SD pipelines, LoRA fine-tuning
- ✅ Stable Diffusion XL: High-resolution image generation and control
- ✅ Advanced Techniques: ControlNet, Inpainting, Image-to-Image translation
- ✅ Style Transfer & Enhancement: Custom style conditioning and super-resolution
- ✅ Production Deployment: Optimized inference, batching, and API integration
Projects: AI Art Generator, Style Transfer App, Real-time Image Enhancement System
💻 2. Full-Stack Development
- ✅ Programming Fundamentals: Python, JavaScript, C++, Java
- ✅ UI Development: React, HTML5, CSS3, Responsive Design
- ✅ API Development: RESTful APIs, GraphQL, WebSockets
- ✅ FastAPI Expertise: High-performance async APIs, dependency injection
- ✅ Backend Integration: Database design, authentication, real-time systems
- ✅ Full-Stack Projects: End-to-end AI-powered web applications
Projects: Multi-Agent Workflow Dashboard, AI Legal Assistant Frontend, Newsletter Automation Platform
🧠 3. Large Language Models (LLMs)
- ✅ LLM Foundations: Transformers architecture, attention mechanisms, tokenization
- ✅ Model Fine-tuning: LoRA, QLoRA, PEFT techniques for domain adaptation
- ✅ Prompt Engineering: Advanced prompting strategies, chain-of-thought, few-shot learning
- ✅ RAG Systems: Vector embeddings, semantic search, document retrieval
- ✅ Advanced Embeddings: Sentence transformers, multi-modal embeddings, re-ranking
- ✅ LLM Architecture: Model optimization, quantization, efficient inference
- ✅ Production LLMs: Deployment strategies, caching, cost optimization
Projects: Multi-Agent RAG System, Legal Document Q&A, Custom ChatBot with Domain Knowledge
🤖 4. AI Agents & Multi-Agent Systems
- ✅ Agent Foundations: ReAct framework, tool use, function calling
- ✅ Multi-Agent Orchestration: Agent collaboration, task decomposition, workflows
- ✅ Agentic RAG: Self-improving retrieval, query planning, dynamic document selection
- ✅ Production Implementation: Scalable agent systems, monitoring, error handling
- ✅ Real-World Applications: Workflow automation in finance, legal, and research domains
🏆 Thesis Project: Built a production-ready Multi-Agent AI System using LLMs & RAG for complex workflow automation with FastAPI backend orchestration
🎯 Final Year Thesis Project | Production-Ready AI System
The Problem: Complex workflows in finance and legal sectors require coordinated execution of multiple specialized tasks—document analysis, data extraction, compliance checking, and report generation. Manual execution is slow, error-prone, and expensive.
My Solution: Architected an intelligent Multi-Agent AI System where specialized LLM agents collaborate to automate end-to-end workflows with minimal human intervention.
🔧 Technical Stack:
- LLMs: GPT-4, Claude, Llama 2 for specialized agent capabilities
- RAG: Vector embeddings (Sentence-BERT) + Pinecone for document retrieval
- Backend: FastAPI with async orchestration and task queuing
- Agent Framework: LangChain + Custom agent orchestrator
- Database: PostgreSQL + Redis for state management
- UI: React dashboard for workflow monitoring
💡 Key Innovations:
- Intelligent Task Decomposition: Master agent breaks complex workflows into subtasks
- Specialized Agent Pool: Document Agent, Analysis Agent, Compliance Agent, Report Agent
- Dynamic RAG: Agents retrieve relevant context on-demand from knowledge base
- Error Recovery: Built-in retry logic and fallback strategies
- Scalability: Designed for production deployment with horizontal scaling
📊 Impact:
- ⚡ 70% faster workflow execution vs manual processes
- ✅ 95% accuracy in document analysis and compliance checking
- 💰 40% cost reduction through automation
- 🚀 Scalable: Handles 100+ concurrent workflows
🎓 Academic Recognition: Presented at college symposium, received excellent thesis grade
🏆 Smart India Hackathon Winner (College Level)
The Challenge: Legal professionals spend hours researching case laws, statutes, and precedents. Law students struggle to understand complex legal language.
Our Solution: Built an intelligent legal assistant that understands natural language queries and provides accurate legal information with source citations.
🔧 Tech Stack:
- NLP: LangChain + GPT-3.5 for query understanding
- RAG: FAISS vector store with Indian Penal Code, case law database
- Backend: FastAPI with document processing pipeline
- Frontend: Interactive React interface with citation viewer
💡 Features:
- 📚 Natural language legal research
- 🔍 Semantic search across 10,000+ legal documents
- 📝 Plain-English explanations of complex legal terms
- 🔗 Source citations with relevant case laws
- 💬 Multi-turn conversational interface
🏆 Achievement: Led team of 4 to win college-level hackathon, impressed judges with AI integration and practical utility
Artistic AI for Creative Professionals
In Development: Building a production-ready Stable Diffusion platform with ControlNet integration, custom LoRA training, and API access for designers.
class MojtabaSiddiqui:
def __init__(self):
self.name = "Mojtaba Siddiqui"
self.role = "AI Engineer | Full-Stack Developer"
self.location = "Aurangabad, Maharashtra, India"
self.education = "B.Tech CSE (AI/ML) @ BATU University"
def current_focus(self):
return {
"learning": [
"Advanced MLOps & Model Deployment",
"Agentic RAG Systems",
"Production AI Architecture",
"Diffusion Model Fine-tuning"
],
"building": [
"Multi-Agent Workflow Automation Platform",
"AI-Powered Newsletter Generator",
"Stable Diffusion API Service",
"LLM Fine-tuning Framework"
],
"exploring": [
"AI Agents for Business Automation",
"Freelance AI Consulting",
"Open Source Contributions",
"Technical Content Creation"
]
}
def available_for(self):
return [
"Full-time AI Engineering Roles",
"Freelance AI/ML Projects",
"Startup Collaborations",
"Research Partnerships",
"Technical Mentorship",
"Open Source Contributions"
]
def fun_facts(self):
return {
"languages_spoken": ["English", "Hindi/Urdu", "Marathi", "Arabic (Basic)", "German (Basic)"],
"interests": ["Chess ♟️", "MMA 🥊", "Movies 🎬", "Badminton 🏸", "Poetry ✍️"],
"community": "Active member of Aurangabad Ploggers (Environmental cleanup)",
"teaching": "Organized AIML workshop for 100+ students"
}
mojtaba = MojtabaSiddiqui()
print(mojtaba.current_focus())DIEMS, Aurangabad | September 2024
- 🎤 Organized college-wide AI/ML hackathon
- 👨🏫 Conducted hands-on session for 100+ students on practical AI topics
- 💡 Topics: LLM fundamentals, RAG systems, AI agent development
- 🏆 Mentored student teams on hackathon projects
Aurangabad Ploggers | Ongoing
- 🌍 Active member participating in weekly heritage site cleanup drives
- ♻️ Combining technology passion with environmental conservation
- 🏛️ Preserving Aurangabad's historic monuments through community action
| Opportunity Type | What I Bring | Ideal Role |
|---|---|---|
| 🏢 Full-Time Roles | Production AI expertise, Multi-agent systems, FastAPI mastery | AI Engineer, ML Engineer, GenAI Engineer |
| 💼 Freelance Projects | End-to-end AI solutions, LLM integration, RAG systems | AI Consulting, Custom AI Solutions, API Development |
| 🚀 Startup Collaborations | MVP development, AI product strategy, Full-stack skills | Technical Co-founder, AI Architect, Early Engineer |
| 🔬 Research Partnerships | LLM research, Agent systems, Academic background | Research Assistant, ML Research Collaboration |
I specialize in transforming AI research into production-ready applications.
Whether you need an intelligent chatbot, a multi-agent automation system, a RAG-powered knowledge base, or custom LLM integration—I can architect, build, and deploy it.
💰 Competitive Rates | 🚀 Fast Turnaround | ✅ Production-Quality Code
Coming Soon: Technical deep-dives on Multi-Agent Systems, RAG implementation, and LLM optimization
- 🏆 Smart India Hackathon Winner (College Level) - AI Legal Assistant System
- 📚 Bachelor's Thesis: Multi-Agent AI System (Excellent Grade)
- 🎓 GPA: 7.52/10.0 in B.Tech CSE (AI/ML)
- 👨🏫 Organized Workshop: Trained 100+ students in AI/ML
- 🌟 Academic Excellence: 90% in SSC, consistent performance throughout education
⭐️ If you find my work interesting, consider starring my repositories!
📧 Open to collaborations, freelance work, and exciting AI projects.
Last Updated: October 2025 | Made with ❤️ by Me