AI Engineer · Agent Systems · LLM Infrastructure
Building production AI systems — EquityLens (92.9% R², in production), ContextIQ (LLM context optimizer), Neuron OS (agent OS). Author of sentinel-cli.
Ahmedabad, India · AI Automation Intern @ Phaze AI · 4th Year CS @ Indus University (CGPA 9.00)
LLM infrastructure, agent orchestration, and evaluation frameworks. Not chatbot demos.
Current Focus:
- LLM context/cost optimization (ContextIQ)
- Multi-agent workflows & MCP tooling
- Vector database internals & RAG optimization
Languages: Python, TypeScript, SQL
Frontend: React, Next.js
Backend: Node.js, FastAPI, Python
AI/ML: PyTorch, Transformers, LangChain, spaCy
Databases: Pinecone, ChromaDB, PostgreSQL, FAISS
Cloud: Docker, Kubernetes, GitHub Actions
Drop-in proxy cutting token spend without code changes. Product spec + TDD suite complete. → Repository
Live system detecting bias in medical ML models. 92.9% R² · 80%+ accuracy. → Repository
Local-first TypeScript OS for autonomous agents. RBAC, sandboxing, credential vault, cost tracking. → Repository
Automated pull request reviews with 5 AI providers, streaming, analytics dashboard, CLI, and GitHub Actions integration. → Repository
9 ANN algorithms (HNSW, IVF, PQ, LSH) hand-rolled in Python. Full RAG pipeline, K8s configs. → Repository
Parse resumes (NER), semantically match to jobs, surface skill gaps, ATS optimization. → Repository
LLM context optimization · MCP Protocol · Agent memory · LLM evaluation · Multi-agent coordination · Vector DB internals