About
Khushal Sahni
Engineer who owns the problem: products, backend systems, and AI agents
I design distributed systems, ship products end to end, and build agent workflows that hold up in production. I care about the economics of what I build, not just the architecture.
What I bring
Backend systems
Payments, checkout, recommendations, and microservices at Urban Company scale. Event-driven designs, explicit failure modes, and ownership of money paths.
Products end to end
Vivaahli: marriage biodata and digital invites. Product, UI, payments, PDF pipeline, and go-to-market. Live consumer product with real families using it.
AI agents
Multi-agent orchestration and tool-calling workflows in production SaaS. Not ML research: shipping agents that call tools, fail safely, and get evaluated.
What I take / what I skip
Good fit
- Full-time roles where I own a surface end to end
- Founding or early engineering teams shipping a real product
- Backend or full-stack work with production stakes (payments, agents, consumer)
- Problems where judgment and shipping beat ticket velocity
Usually a pass
- Pure ML research or model training without a product
- Roles that are only CRUD and meetings
- Agencies that need a pair of hands, not an owner
Track record
SDE 2 · Fello
Feb–May 2026
Building backend infrastructure from the ground up: communication systems, AI integrations, and multi-agent orchestration at an early-stage SaaS.
SDE 1 → SDE 2 · Urban Company
~4 years
Backend and full-stack work on payment systems, checkout, recommendations, and microservices at scale, through IPO readiness.
Indian Institute of Technology (BHU) Varanasi
B.Tech · 2022
Selected work
- Vivaahlilive
Wedding-tech for Indian families
Biodatas created: 1.4K+ · Languages: 10 · Family rating: 4.8/5 · Time to finish: ~5 min
- ourmoneylive
Where did the reported rupee for my place go?
Hackathon: Build What Moves India · Shortlisted from: ~5,000 · Data: Mock + live extract · RTI: Drafts, never files
- Zashiki Warashilive
A house spirit for the localhost pile
Platform: macOS-first · Catalog: Machine-local SQLite · Rust tests: 44 · License: MIT
- AniRecoarchived
Personalized anime recommendations from your AniList
Hybrid ranker: 5 signals · Retrieval: 3 vectors · Catalog: 50k+ anime · Outcome: Archived
Next step
Read the resume, skim a case study, or email me directly.