B.Tech CSE (AI & DS Specialization) | Architecting Agentic Systems & LLM-Powered Applications Developing intelligent workflows through RAG, Large Language Models, and Scalable Software.

I am a 3rd-year Computer Science student specializing in the intersection of Full Stack Development and Applied AI. My focus is on building systems that don't just process data but understand it—leveraging LLMs, RAG pipelines, and Agentic workflows to solve high-complexity technical challenges.

• Intelligence Orchestration: Developing RAG (Retrieval-Augmented Generation) pipelines to provide LLMs with precise, local context.
• Agentic Design: Exploring and implementing autonomous agent workflows for deterministic task execution.
• Full-Cycle Data Proficiency: Advanced data manipulation with Pandas/NumPy and predictive modeling with Scikit-Learn.
• Architectural Versatility: Building high-performance backends (FastAPI, Django), responsive web frontends (React), and cross-platform mobile applications (Flutter/Dart).
• Deployment Orchestration: Leveraging Docker for containerization to ensure scalable and reproducible environment orchestration.

Category	Specialized Skills
AI & LLM Stack
Languages
Frameworks
Data Science
Databases
Cloud & DevOps

• The Problem: LLMs often struggle with deep, project-specific context in large repositories.
• The Solution: A RAG-based system that indexes local source code into a vector database, enabling precise, context-aware debugging and architectural explanations.
• Core Tech: Python, LangChain, Vector Stores, OpenAI / Open-source LLMs.
• Impact: Bridges the gap between generic AI knowledge and private codebase logic.

• The Problem: Dense technical and legal papers require significant human synthesis time.
• The Solution: An advanced summarization engine leveraging Large Language Models and Transformer architectures to distill core insights with zero context bleed.
• Core Tech: Python, HuggingFace, PyTorch, Streamlit.
• Impact: Reduced document synthesis time by ~80% during pilot testing.

• Concept: A context-aware web application for zero-effort itinerary planning.
• Tech Stack: Django, Python, Google Maps API, JavaScript.
• Impact: Managed multi-constraint resolution for budget and geographical optimization.

• GirlScript Summer of Code (Extd 2024): Ranked #86 Globally.
  • Optimized system workflows and resolved technical debt across major OS ecosystems.
• 100 Days of Code (AI & Data Science): 100+ consecutive days of building and deploying functional AI/ML utilities.

• Agentic AI: Developing autonomous "Developer Agents" capable of multi-step task planning.
• SLM Optimization: Exploring Small Language Models (SLMs) for efficient, low-latency inferencing on edge devices.
• RAG Architecture: Scaling CodeRAG to handle microservice architectures with inter-dependent repositories.

Seeking Summer 2026 Internships and R&D roles in AI/ML Engineering.

"Building the future, one token at a time."
© 2026 Sanchit Chauhan