Shagun Khemka, Senior Software Engineer at JioHotstar

Hey, I'm Shagun Khemka.

I build backend platforms that stay fast under heavy load, and production AI systems that stay cheap and correct.

Senior Software Engineer at JioHotstar, working on the subscriptions platform. Previously FamPay (YC S19) and Instamojo. Based in Mumbai.

Experience

JioHotstarSenior Software Engineer, Backend
  • 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)
  • Led the cross-team migration of 14M+ JioCinema subscribers
  • 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
  • ~20% compute savings from a Graviton + Java 21 migration, packaged as Claude and Codex agent skills for self-serve rollout
  • Helped build the JioCinema entitlement service serving user attributes to core services (Go, Spanner, Redis); team won “Gangstar of the Quarter” (2024)
  • Automated the Disney+ Hotstar → Disney+ plan migration (6K+ config changes across 25+ markets), cutting ~3 hours of manual launch-day work to under 15 minutes including verification; reused by a teammate for the follow-on market handover
  • 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, 20–30% 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)
2019 – 2020

Activity

1,360 contributions in the last yeargithub.com/S2606 ↗
NovDecJanFebMarAprMayJunJulAugSepOctDec 25: 7 contributionsDec 27: 3 contributionsJan 1: 6 contributionsJan 2: 7 contributionsJun 3: 13 contributionsJun 4: 20 contributionsJun 5: 16 contributionsJun 6: 63 contributionsJun 7: 26 contributionsJun 8: 56 contributionsJun 9: 35 contributionsJun 10: 10 contributionsJun 11: 19 contributionsJun 12: 28 contributionsJun 13: 45 contributionsJun 14: 28 contributionsJun 15: 25 contributionsJun 16: 12 contributionsJun 17: 7 contributionsJun 18: 8 contributionsJun 19: 25 contributionsJun 20: 10 contributionsJun 22: 15 contributionsJun 23: 32 contributionsJun 24: 45 contributionsJun 25: 18 contributionsJun 26: 4 contributionsJun 27: 21 contributionsJun 28: 7 contributionsJun 29: 22 contributionsJun 30: 10 contributionsJul 1: 5 contributionsJul 2: 20 contributionsJul 3: 6 contributionsJul 5: 1 contributionJul 6: 16 contributionsJul 7: 12 contributionsJul 8: 4 contributionsJul 9: 15 contributionsJul 11: 42 contributionsJul 12: 8 contributionsJul 13: 1 contributionJul 14: 10 contributionsJul 16: 13 contributionsJul 17: 1 contributionJul 19: 13 contributionsJul 20: 14 contributionsJul 21: 6 contributionsJul 22: 5 contributionsJul 23: 2 contributionsJul 24: 14 contributionsJul 25: 2 contributionsJul 26: 6 contributionsJul 27: 11 contributionsJul 28: 21 contributionsJul 29: 16 contributionsJul 30: 1 contributionJul 31: 13 contributionsAug 1: 13 contributionsAug 2: 24 contributionsAug 3: 1 contributionAug 4: 1 contributionAug 7: 8 contributionsAug 8: 5 contributionsAug 9: 45 contributionsAug 10: 10 contributionsAug 14: 1 contributionAug 17: 5 contributionsAug 18: 4 contributionsAug 19: 19 contributionsAug 20: 5 contributionsAug 21: 9 contributionsAug 22: 14 contributionsAug 23: 11 contributionsAug 25: 5 contributionsAug 26: 3 contributionsAug 27: 1 contributionAug 28: 1 contributionAug 31: 5 contributionsSep 1: 4 contributionsSep 3: 5 contributionsSep 4: 3 contributionsSep 8: 4 contributionsSep 9: 1 contributionSep 11: 8 contributionsSep 12: 26 contributionsSep 13: 12 contributionsSep 15: 5 contributionsSep 16: 3 contributionsSep 17: 4 contributionsSep 19: 10 contributionsSep 22: 3 contributionsSep 23: 1 contributionSep 24: 8 contributionsSep 25: 1 contributionSep 27: 8 contributionsSep 28: 1 contributionSep 29: 2 contributionsOct 2: 32 contributionsOct 3: 3 contributionsOct 5: 1 contributionOct 6: 3 contributionsOct 7: 4 contributionsOct 10: 4 contributionsOct 12: 1 contributionOct 13: 5 contributionsOct 15: 8 contributionsOct 16: 4 contributionsOct 17: 1 contributionOct 18: 10 contributionsOct 19: 1 contributionOct 20: 16 contributionsOct 21: 7 contributionsOct 22: 7 contributionsOct 23: 2 contributionsOct 25: 19 contributionsOct 26: 2 contributionsOct 28: 10 contributions

Projects

Document extraction platformin production

private

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

Python · Django · Postgres · GPT / Gemini (production) · Claude, Qwen, Mistral, Sarvam (evaluated) · Pulumi · Prometheus + Grafana

ConsumerBuddyprivate beta

consumerbuddy.in ↗

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.
Multi-model routingVisionTool useCost controlsLLM observability

Next.js · FastAPI · Postgres · Claude Sonnet + Haiku (production) · GPT, Gemini, Sarvam (integrated) · AWS via Pulumi · Prometheus + Grafana

Open source, education and certifications → Background