Architected and own the P0 entitlement read path at ~50K RPS, holding P99 SLAs on ~20% fewer pods
Re-architected purchase and upgrade flows for ~20% revenue growth; team won “Astral Team Award” (2026)
Designed a decoupled payment-notification architecture so Ads and partner platforms consume payment events independently
Led the cross-team migration of 14M+ JioCinema subscribers
Helped build the JioCinema entitlement service serving user attributes to core services (Go, Spanner, Redis); team won “Gangstar of the Quarter” (2024)
~20% compute savings from a Graviton + Java 21 migration, packaged as Claude and Codex agent skills for self-serve rollout
Led a SEV-1 response at JioCinema when Akamai blocked ~200K payment-gateway renewal webhooks; restored ~70K renewals within 3 hours by fixing the reconciliation cron, which was calling the wrong status-check API
IPL on-call at 30M+ concurrent viewers · LLD/HLD hiring panelist (20+ interviews) · ran Go and Spanner knowledge-sharing sessions that got 3 teammates contributing independently
2024 – now
FamPay (YC S19)Software Engineer, Backend
Joined as the second backend engineer; led a team of four on FamCard: 5M+ virtual cards, ₹10M+ daily transactions, ~30% lower delivery cost
Architected the gift card platform (₹60 Cr+ cumulative GMV) and built recharge and bill-payment products, around 60% of monthly revenue
30+ backend interviews; onboarded new engineers and mentored 5+ interns and juniors into full-time roles
2020 – 2024
InstamojoSoftware Engineer, Backend
Automated merchant onboarding, cutting KYC resubmissions by ~25%
Built reseller product ingestion that onboarded ~15K products in its first month; converted from intern to full-time (PPO)
An LLM pipeline, built for a logistics ERP software company, that turns scanned, handwritten consignment notes into validated, structured data. I designed, built and run it on my own.
Evals pick the model: every model, prompt or provider change is scored field by field against a labelled golden set before it ships. I evaluated Claude, GPT, Gemini, Qwen, Mistral and Sarvam side by side, and the cheapest models that got every ledger-critical field right (total amount, invoice value and actual weight) went to production.
Phased model rollout: production runs Gemini 3.8 Flash and GPT-6 Luna, switched tenant by tenant from the admin panel with no redeploy. On the latest eval, GPT-6 Luna is 8× cheaper and ~45% faster than Gemini Flash, with those ledger-critical fields at 100%.
Guardrails and accept policy: GSTIN checksum and state checks, date and total reconciliation, and fuzzy station matching. A document is auto-added only when every mandatory field passes; otherwise it's flagged for human review with the exact fields named.
Gateway, cost and observability: Anthropic, OpenAI, Gemini, Mistral and Sarvam sit behind one interface, and tenants can bring their own keys. Every call is metered per tenant, and dashboards track cost per document and latency.
Model selectionEvalsStructured outputsHuman-in-the-loopLLM cost metering
An AI product that helps Indian consumers draft and file consumer-court complaints without a lawyer. I built and run it end to end.
Vision and tool use: Claude reads uploaded evidence (PDFs and photos) to write court-format annexure descriptions, and uses its web-search tool to find the opposite party's registered address and legal contacts.
Runtime model routing: a different model per task (Sonnet drafts, Haiku handles lookups and vision), configurable per user cohort from the admin panel without a redeploy. Each paid document pins the model plan it was drafted with, so re-renders are reproducible.
Cost and abuse controls: a per-call cost ledger in INR, hourly and daily AI caps per user, and input limits on every AI feature.
LLM observability and legal review: calls, tokens, latency, errors and INR cost tracked per feature, provider and model. Drafts go to a consumer-law advocate for sign-off before public launch.
JioHotstar
FamPay (YC S19)
Instamojo