I'm a passionate developer and B.Tech. graduate from IIT Madras, specializing in AI Prompt Engineering, Machine Learning, Natural Language Processing, and Full-Stack Web Development. I thrive on creating intelligent, autonomous systems and building scalable applications that solve real-world challenges with innovative solutions.

• 🎓 Education: B.Tech from IIT Madras
• 💼 Current Role: GenAI Prompt Engineer at Klarity.ai - Building powerful GenAI automations at unprecedented speed and scale
• 🏆 Competition Track Record: 3rd place (ML for Marine Autonomy) | 4th place (Pravartak Datathon)
• 🔭 Currently Working On: Agentic AI systems, RAG architectures, and voice-first applications
• 🌱 Currently Learning: Advanced prompt engineering techniques, multi-agent systems, and LLM orchestration
• 💡 Expertise: Prompt Engineering, Context-Aware Generation (CAG), LLM fine-tuning, and AI workflow automation

An open-source, AI-powered application using Agentic CAG to chat with any public GitHub repository or developer profile, offering deep code analysis, visual architecture maps, and security audits.

🎯 Key Highlights:

• Context-Aware Engine (CAG): Intelligently selects relevant code snippets instead of loading entire repositories
• Visual Architecture Maps: Auto-generates Mermaid flowcharts from complex code logic for instant visualization
• Deep Profile Intelligence: Analyzes coding styles, commit patterns, and developer habits across entire portfolios
• Zero-Config Security Audits: Detects vulnerabilities with AI-powered triage and actionable fix recommendations
• Mobile-First Design: The only tool in its class optimized for on-the-go code reviews

💡 Why It Stands Out:

• ✅ Works instantly on any public repo—no installation, login, or GitHub App required
• ✅ Uses Context Augmented Generation (CAG) vs traditional RAG for superior code understanding
• ✅ Generates interactive visuals instead of text walls
• ✅ Analyzes developers, not just code

🛠️ Tech Stack: Next.js • Google Gemini • Vercel KV • TypeScript • Tailwind CSS • Framer Motion

A voice-first to-do list application for intuitive task management entirely through natural language commands, powered by Deepgram for real-time transcription and Groq's qwen-2.5-32b for AI command analysis.

🎯 Key Highlights:

• Voice-First Interface: Create, update, and manage tasks using natural language—no typing required
• Real-Time Transcription: Leverages Deepgram's blazing-fast speech-to-text for instant feedback
• AI-Powered Command Analysis: Understands complex intents like "remind me to buy groceries tomorrow at 5pm"
• Client-Side Intelligence: Local models for instant priority detection and date parsing
• Seamless UX: Combines Groq's speed with intuitive animations for a smooth experience

📊 Impact:

• ⚡ 95%+ transcription accuracy with sub-second latency
• 🎯 Handles 20+ natural language command variations
• 📱 Fully responsive with touch and voice support

🛠️ Tech Stack: Next.js • React • Deepgram • Groq (qwen-2.5-32b) • ShadCN/UI • Tailwind CSS • Framer Motion

An advanced web application providing expert-level road safety intervention recommendations using Retrieval-Augmented Generation (RAG) architecture with Google's Gemini LLM and Vertex AI Search.

🎯 Key Highlights:

• RAG Architecture: Grounded recommendations from a curated knowledge base of road safety research
• AI Orchestration: Leverages Google Genkit to manage the entire AI workflow seamlessly
• Evidence-Based Advice: Provides detailed rationale with direct citations from authoritative source documents
• Optimized Query Flow: Brainstorms multiple search queries and executes parallel searches for reduced latency
• Domain Expertise: Built on real-world road safety data and best practices

📊 Impact:

• 📚 Processes 100+ authoritative safety documents
• ⚡ 60% faster response time through parallel search optimization
• 🎯 Provides source-backed recommendations with verifiable citations

🛠️ Tech Stack: Next.js • React • TypeScript • Tailwind CSS • ShadCN UI • Google Genkit • Gemini 2.5 Flash • Vertex AI Search • Firebase App Hosting

An innovative web application that streamlines the process of transforming raw ideas into structured, developer-ready plans using generative AI.

🎯 Key Highlights:

• AI-Powered Idea Extraction: Converts concepts from text, images, or PDFs into structured summaries
• Adaptive Questionnaire: AI product manager refines ideas with non-technical, multiple-choice questions
• Intelligent Tech Stack Suggestions: Recommends optimal tech stacks based on project requirements
• Developer-Ready Brief: Generates complete project setup prompts, file structures, and sequential engineering prompts
• End-to-End Workflow: Takes you from "I have an idea" to "Here's the implementation plan"

📊 Impact:

• ⏱️ Reduces project planning time by 70%
• 🎯 Supports 10+ input formats (text, images, PDFs)
• 🔧 Generates actionable developer prompts ready for AI coding tools

🛠️ Tech Stack: Next.js • TypeScript • Google Genkit • Gemini AI • React • ShadCN UI • Tailwind CSS • Firebase (Firestore, Auth)

An AI-powered system for extracting cancer-related information from patient Electronic Health Record (EHR) notes, focusing on information retrieval and structured medical data extraction.

🎯 Key Highlights:

• Multi-Stage Information Retrieval: Combines keyword search (BM25) with semantic search (Sentence Transformers, CrossEncoder)
• LLM-Based Data Extraction: Uses quantized Qwen/Qwen2.5-7B-Instruct-1M for structured JSON output
• Robust Error Handling: Implements advanced mechanisms for JSONDecodeError recovery
• GPU Efficiency: Utilizes 4-bit quantization for running 7B parameter models on T4 GPUs
• Medical-Grade Accuracy: Designed for precision in clinical information extraction

📊 Impact:

• 📄 Processes complex medical notes with 90%+ extraction accuracy
• ⚡ Runs efficiently on consumer GPUs through quantization
• 🎯 Extracts structured data from unstructured clinical text

🛠️ Tech Stack: Python • NLTK • BM25 • Sentence Transformers • CrossEncoder • Qwen LLM • bitsandbytes • PyTorch

An LLM-powered tool that enables users to extract information, transcribe, and ask questions about YouTube video content, providing a seamless way to interact with video transcripts.

🎯 Key Highlights:

• Speech-to-Text: Utilizes OpenAI Whisper for high-quality, multilingual audio transcription
• Audio Extraction: Employs FFMPEG for efficient audio extraction from video streams
• NLP-Driven Querying: Leverages Hugging Face Transformers for natural language understanding and query resolution
• Scalable Design: Architected for multilingual transcription, advanced summarization, and AI-powered Q&A

📊 Impact:

• 🌐 Supports 50+ languages through Whisper
• 📝 Generates accurate transcripts and summaries
• 🎯 Enables semantic search across video content

🛠️ Tech Stack: Python • OpenAI Whisper • Hugging Face Transformers • FFMPEG • NLP

I believe in building AI systems that are:

• 🎯 Purpose-Driven: Every line of code should solve a real problem
• 🧠 Intelligently Designed: Leverage AI where it adds genuine value, not as a buzzword
• ⚡ Performance-First: Optimize for speed and efficiency without sacrificing functionality
• 📱 User-Centric: Complex technology should feel simple and intuitive
• 🔓 Open & Collaborative: Knowledge grows when shared

"The best AI is invisible—it just works."

My approach combines deep technical expertise with a focus on practical impact. Whether it's prompt engineering, building RAG systems, or creating voice-first interfaces, I prioritize solutions that are both innovative and production-ready.

I'm always excited to collaborate on interesting projects, discuss AI/ML innovations, or explore new opportunities!

• 💼 LinkedIn: linkedin.com/in/127001-sameer
• 📧 Email: pieisnot22by7@gmail.com
• ⏱️ Response Time: Within 24 hours
• 🎯 Open To: Hackathons • ML Competitions • Collaborative Projects • AI Consulting

• 🏆 Hackathons & Competitions: Love building innovative solutions under pressure
• 🤝 Open Source Collaborations: Interested in contributing to impactful AI/ML projects
• 💡 Freelance Opportunities: Prompt engineering, RAG systems, and AI automation
• 🎓 Knowledge Sharing: Speaking engagements, workshops, or technical writing