Work Experience
Professional experience across AI systems, full-stack engineering, and product development
Garuda Robotics
Case study
AI Engineer Intern · Singapore
Building an agent that plans drone inspection flights. A planner describes what they need in plain English, answers three or four questions, and gets back a flight plan already checked against the airspace they are cleared for. Before this, someone worked out a hundred-odd commands by hand.
I own it end to end: the app the planner uses, the service behind it, and the layer that decides whether a request is allowed to go through at all.
Most of the work has gone into that last part. The thing at the other end of this is a drone rather than a document, which changes where the checks have to live. That's written up as a case study.
- Agentic AI
- LLMs
- MCP
- LangGraph
- FastAPI
SP Digital
Case study
Data Science Intern · Singapore
Six months on the guardrails for an internal assistant used by frontline staff at a utility, plus the adversarial test suite that showed whether they worked. Unsafe responses dropped roughly 60%.
The people using it are out doing physical work on the grid. Some of them can see things the others shouldn't, so a lot of the job was working out what the assistant says to whom.
This was the first thing I'd built where someone was paid to break it, and it changed what I think the work is. I've written that up properly as a case study.
- LLM Systems
- Guardrails
- Enterprise AI
- Evaluation
Lecture AI
Case study
Co-Founder · Singapore
Co-founded a startup that turned lecture recordings into bilingual study notes. We built what students asked for and then hit a problem that had nothing to do with the product.
- AI
- NLP
- RAG
- Startup
- Product
KPMG
AI Labs Intern · Gurugram
Built an agentic RAG system using LangChain and Azure OpenAI for document retrieval across 200+ internal consulting documents. Shipped source-PDF retrieval and structured Excel extraction workflows, reducing knowledge lookup time by ~40–50% for 12+ person teams.
A twelve-person consulting team kept everything it knew in a shared document store. Over 200 files, findable only if you already knew roughly what you were looking for and what someone had called it. The cost wasn't dramatic. A few minutes lost, several times a day, across everyone, plus the invisible cost of redoing work because you couldn't find the thing that already existed.
I built a system that let them ask a question instead of searching. It's an agentic setup rather than a single retrieval pass. It decides what to look for, retrieves, and goes back for more if the first pass doesn't answer the question. That mattered here, because consulting questions usually need pieces from several documents rather than one good paragraph.
The chunking took a few attempts to get right. Long PDFs split naively produce chunks that end mid-argument, and the model fills the gap with something plausible. I moved to 1000-token chunks with 200 tokens of overlap, so a point that straddles a boundary appears whole in at least one chunk. That fixed most of the wrong answers I was seeing.
The bigger thing I underestimated was trust. Consultants didn't just want the answer, they wanted to know where it came from, which is fair when their name goes on what they send a client. So every response returns the top four supporting documents as links alongside it, and spreadsheets are handled properly so numbers come back as numbers rather than mangled text.
Retrieval made the answers fast. Being able to check them is what made anyone use them. Lookup time dropped by 40 to 50 percent.
- RAG
- LangChain
- Azure OpenAI
- Enterprise
AlygnAI
SDE Intern · Remote (US)
A remote internship with a Bay Area startup, run alongside a full-time one in India. Same summer, two jobs.
I ran this alongside KPMG. Same summer, one in person and full time, this one remote. I don't recommend it, but I wanted it badly enough to try.
The reason was specific. I'd read a lot about how Bay Area startups work and I wanted to find out whether the version in my head matched the real thing. So I spent that spring cold-applying to American startups from the other side of the world, which is a slower and more discouraging process than it sounds, and eventually got one.
What I found was mostly what I'd hoped for. The team was small and moved quickly, and nobody had time to hand me a scoped task and check on it. I ended up owning a migration from a prototype built on a no-code tool to a real backend, start to finish. That's more ownership than I'd have got anywhere with more people in it, and it's the thing I'd point at from that summer. I also compared two ways of adapting a language model to their domain, fine-tuning it on their data against retrieving from their documents at query time, and wrote up the tradeoff for the founding team's architecture decision.
The double summer was probably one summer too many. It was also the only way I was going to find out what I wanted to know.
- FastAPI
- Auth
- LLMs
- Startup
StatusNeo
SWE Intern · Gurugram
Developed REST APIs in Spring Boot for enterprise banking client with JWT authentication and RBAC. Wrote JUnit tests in agile production environment.
- Java
- Spring Boot
- REST APIs
- Enterprise