Senior Engineer @ US software company · remote

Software Engineer.Ship AI that works. Design systems that last.

I've built payments, marketplaces and ledgers.Now I build the LLM systems that sit on top of them.Because a clever prompt never survives a bad architecture.

  • LLM & RAG systems
  • Agentic workflows
  • Enterprise blockchain
Santosh Bhulun

Senior Software Engineer working on production Generative AI, agentic pipelines and enterprise blockchain for a US company, remotely from Kathmandu. Polyglot by necessity: Go for services, TypeScript for products, Python for models, PHP where the money still lives. I care about retrieval quality, evaluation and boring, observable infrastructure more than about the model of the week.

Track record

Period / organisationWhat I actually didReal impact

2023 — Pres.

US Software Company

Senior Software Engineer · Remote

Production LLM applications and Retrieval-Augmented Generation for enterprise data. Advanced RAG pipelines, agentic workflows and an evaluation framework so we know when retrieval gets worse, not just when it gets slower. Go services and Hyperledger Fabric chaincode underneath.

Production GenAI

2022 — 2023

Swivt

Full Stack JavaScript Developer · Hong Kong

End-to-end delivery on the MERN stack for client products, from MongoDB data modelling to React front-ends. Earlier in the same company: Laravel APIs with Vue.js front-ends.

Full-stack delivery

2019 — 2022

F1Soft International

Software Engineer · Kathmandu

Fintech web applications and internal tooling for one of Nepal's largest digital payment providers. Grew from Associate to Software Engineer while shipping on payment and banking products where a bug is a customer's money.

Payments at scale

2019

Yashri Soft

Full Stack Laravel Developer · Kathmandu

Full-stack web applications with Laravel and MySQL, taken from Adobe XD mock-ups to deployment.

First production code

Principles

AI is an engineering discipline.

  • Evaluate before you ship

    An LLM feature without an eval set is a demo. I build the faithfulness and recall checks first, then the feature, then the dashboard that tells us when it drifts.

  • Retrieval is the product

    Most 'the model hallucinated' bugs are 'we retrieved the wrong chunk' bugs. Chunking, hybrid search and reranking get more of my attention than prompts.

  • Boring infrastructure wins

    Go services, queues, idempotent handlers, structured logs. The exciting part of an AI system should be the answers, not the incident channel.

  • Own the whole loop

    Ingestion, retrieval, orchestration, evaluation, deployment. Splitting them across five people is how you end up with a pipeline nobody understands.

Toolbox

Languages
Go · TypeScript · Python · PHP · SQL
AI
LLM apps · RAG & Advanced RAG · agents · evals · vector search
Backend
Node.js / Nest.js · Laravel · gRPC · event-driven · CQRS · DDD
Frontend
React · Next.js · Vue.js
Blockchain
Hyperledger Fabric · Go chaincode
Infra & data
Docker · Kubernetes · CI/CD · PostgreSQL · MongoDB · Redis

Side projects

Contact

Got a hard problem?
Bring the ugly version.

Senior roles, contract work, or a second opinion on a RAG pipeline that “mostly works”. One email, no form, no calendar link.

santoshbhulun@gmail.com

Replies within a day or two. Your data stays between us.