Building multi-agent architectures, RAG pipelines, and end-to-end ML systems using LangChain, LangGraph, and PyTorch.
Based in Vadodara, India.
A glimpse into my background, core expertise, and current availability.
I specialize at the intersection of Machine Learning and autonomous Agentic workflows. From building robust LangChain orchestration engines to deploying low-latency PyTorch models, I bridge the gap between AI research and highly scalable, production-ready applications.
3rd-year B.Tech CS Student consistently ranking among the top peers in quantitative engineering.
Ready for full-time engineering roles & freelance AI architecture.
My professional journey, academic background, and notable achievements.
CHARUSAT University, Gujarat — Deep Learning & Medical Imaging
Building an AI-based system for affordable eye disease screening in rural India via retinal fundus image analysis.
Designing a deep learning classification model using MobileNetV3Large with two-stage transfer learning to identify 4 conditions (CNV, DME, Drusen, Normal) using mixed precision.
Engineering a comprehensive multi-metric evaluation framework producing confusion matrices, ROC/AUC curves, and per-class F1 scores.
CHARUSAT University, Gujarat
Production AI systems & research. Click any project to explore full architectural case studies.
AI Agent Orchestration
Production-grade AI SaaS platform automating LinkedIn workflows using LangChain and real-time Socket streaming.
✦LangSmith Tracing, real-time sync
Case StudyFull Stack ML Application
End-to-end AI-driven platform for interview preparation using deterministic LLM evaluation matrices.
✦500+ Reports Generated
Case StudyAI Conversational Agent
A powerful Retrieval-Augmented Generation chatbot managing domain-specific university admission protocols.
✦Reduced query wait time by 90%
Case StudyDeep dives into real-world ML systems, AI architectures, and engineering challenges — written for engineers who build.
One person. Zero employees. Full-scale output. Here's the exact AI stack — LLMs, agents, automation, content, and revenue tools — that lets a single operator run a real business in 2025.
80% of RAG failures happen at the chunking layer, not the LLM. Here's how to move from fixed-size splitting to intelligent, context-aware chunking.
What actually happens inside an embedding model? Tokenization, lookup tables, positional encoding, multi-head attention, pooling, contrastive training — every layer explained with diagrams and code.
Agentic AI promises autonomy, but prompt injection, tool misuse, and broken trust chains are silently killing deployments. Here's what's really broken and how to fix it.
Tools I use to build scalable intelligent systems.
LangChainLangGraph
CrewAI
Scikit-learn
TensorFlow
HuggingFace
Python
C++
Next.js
Supabase
MongoDB
SQL/Postgres
Git
Docker
PostmanLangSmith
n8nLangChainLangGraphCrewAIScikit-learnTensorFlowHuggingFacePythonC++Next.jsSupabaseMongoDBSQL/PostgresGitDockerPostmanLangSmithn8nOpen to ML/AI internships, collaborations, and real-world projects. I enjoy working on real-world AI problems and building systems that actually make an impact.
Have an idea or opportunity? Let's talk.
© 2026 Krishil Agrawal. All rights reserved.