P
Bengaluru, KA, India
PythonSQLMachine LearningAI
Z
Role overview
Job description
About Zenskar Zenskar is an AI-native revenue automation platform built to handle real-world complexity. It sits between your CRM and ERP automating everything in between: billing, revenue recognition, collections, usage metering, and analytics. Across any pricing model, any entity structure, any currency. Our vision: Zero-Touch Finance. Agents execute. Humans supervise. Finance teams set the rules. Zenskar runs them. Founded by Apurv Bansal and Saurabh Agarwal , second-time founders whose previous startups were acquired by Snapdeal and Gaana. The team brings experience from Google, Deutsche Bank, Elevation Capital, IIT Bombay, IIT Delhi, and Harvard Business School. We have 5x'd revenue in the past year. We are default alive . Funding We raised $15M in Series A funding in April 2026, led by Susquehanna Venture Capital, Bessemer Venture Partners, Shine Capital, and Rho, with participatio
…About this listing
- Listed from Zenskar's careers site via Keka · last confirmed on the source 25 Oct 2026
- Posted 13 Aug 2026
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Concretely: this role exists so that no team ever ships an LLM integration that hallucinates a wrong invoice amount, or burns unbounded API cost, because there was no standard pattern to build against; This is LLM application engineering: building real product features on top of frontier model APIs and making them production reliable; What you'll do Turn ambiguous product asks into working LLM-based features: prompt and context design, tool calling, orchestration across model calls, structured output handling Design for the failure modes non-deterministic systems actually have: hallucination, drift, silent wrongness; Build guardrails, and know the difference between demo-grade and production-grade because you've shipped the gap between them Make sure an LLM is never the unverified source of truth for a financial decision
Department · Engineering
Hiring company
Zenskar
Finance & Banking
AI-native billing and RevRec automation for real-world complexity — without workarounds. Agents execute, humans supervise. That's Zero-Touch Finance.
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