Prev @ Multibagg AI · National Finalist IFF-FinTech Olympiad’24 · IITP'27
Working on AI Agents, Quant and Backend
Founder's Office & AI Engineer
PineconeQdrantRedis
BullMQGrafanaAzureDockerJan 2025 - Jun 2026 · 1 yr 5 mos
Worked directly with the founder to build and scale production AI systems across agent orchestration, retrieval, evaluation, financial data and user-facing product workflows.
Bachelor of Technology
Aug 2023 - May 2027 · 3 yrs 9 mos
Major: Computer Science and Engineering
Minor: Data Science and Artificial Intelligence
Razorpay AI Buildathon · verified Test Mode recovery flow
An explainable revenue-recovery system for Razorpay merchants: AI proposes one bounded action, deterministic policy guards execution, and durable workflows follow failed payments to auditable outcomes.
Frontend · Nifty 50 paper-trading arena
A read-only research dashboard where LLMs paper-trade the Nifty 50 under realistic constraints, with rankings, trade history, portfolio analytics and deterministic Redis-backed replay.
BullMQPineconeAzurePostgreSQLStructured event intelligence for Indian equities
A financial event-intelligence platform that organizes filings, disclosures and news into structured, traceable events with materiality and source context for Indian-equity research.
Evidence-first analysis · reports, transcripts and frame sampling
A multi-agent analysis pipeline that ranks public Instagram posts, extracts video and audio evidence, and turns measurable creative patterns into an adaptable strategy report.
Issue → validated patch → draft PR
An agentic contributor assistant that scopes Go issues, scans repositories, generates and validates focused patches, and prepares draft pull requests behind explicit safety gates.
Production toolkit
The languages, frameworks and infrastructure behind my work.
hover a mark ✦
BullMQPinecone
Mine The Model · Celesta IIT PatnaStock-price ML contest by NJack ML IIT Patna & Cynaptics IIT Indore — beat the benchmark.
6-week Elementary & Advanced quant finance programme by Quant Club, IIT Kharagpur.
A practical design for bounded agent loops: finish when the work is good enough, compact before context degrades, and stop safely when progress stalls.
A practical architecture for agents that turn experience into safer, measurable improvements through memory, sandboxed experimentation, evaluation, and replay.
