What Building Real AI Systems Taught Me (That Courses Don't)
From models to production: lessons from shipping AI used by real users.
Read on MediumMulti-agent workflows using LangGraph, LangChain with tool-calling, memory, planning, and state transitions.
Fast AI backends: async pipelines, WebSocket streams, GraphQL and REST APIs, real-time AI integrations.
End-to-end ML pipelines from data preparation to deployment, model monitoring, and retraining workflows.
Applied research in legal AI and healthcare, including fine-tuning BERT, LLaMA, and building custom evaluation workflows.
Real systems, shipped to production.

Full-stack cybersecurity investigation platform using React, FastAPI, Neo4j, and graph analytics. Unifies SOC, AML, and identity signals into a single graph. AI agents run autonomous investigations and auto-generate FIU-IND Suspicious Transaction Reports, cutting analyst workload from hours to minutes.

ML + Operations Research platform improving fleet utilization to 94.6%. Combines RandomForest demand prediction (F1=0.93) with OR-Tools route optimization, explainable agentic decision workflows, and scenario simulation engine for dynamic routing strategies.

Multi-agent conversational AI system using LangGraph orchestration. Hybrid RAG pipeline (FAISS + BM25) with source-grounded responses, multi-layer memory including episodic, long-term, and semantic recall. 1.2–1.5s latency with safety guardrails and escalation logic for high-risk emotional states.

AI-powered interactive digital museum with a gamified isometric pixel-art environment and multilingual agentic RAG guide. Users explore Indian heritage through personalized tours in 10+ languages. Winner of the Experience India Track at IndiaStack Build for Billions Hackathon at NITK Surathkal.

Cross-platform fest app (Android, iOS, Web) with 200+ users. Built Polaroid-style memory frames, live map event navigation, gamified QR stall interactions, and vision-based photo clustering using FaceNet so attendees could find photos of themselves automatically. Led backend systems and deployments.
Agentic misinformation detection system that identifies and verifies viral claims during global crises. Specialized AI agents monitor social media and news in real time, cross-checking claims against credible sources. Builds dynamic reputation scores for information sources, enabling faster trust decisions at scale.
3 wins, 4 podium finishes. Problems solved under pressure.
From foundations to scale. A journey in building AI.
Built strong foundations in machine learning, deep learning, and backend engineering through coursework, certifications, and hands-on projects. Focused on understanding core ML concepts while actively building.
Currently building production AI systems: agentic workflows, LLM pipelines, and applied research in legal AI and healthcare monitoring. Focused on shipping real products.
Aim to scale AI systems, publish research, and contribute to open-source ecosystems. Building toward global AI engineering or applied research opportunities.
Thoughts on building AI systems, research, and engineering.
From models to production: lessons from shipping AI used by real users.
Read on MediumLessons from building real multi-agent AI systems and why the paradigm shift matters.
Coming SoonPractical insights from fine-tuning LLMs for long-form legal documents at scale.
Coming Soon