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.
Funding
We raised $15M in Series A funding in April 2026, led by Susquehanna Venture Capital, Bessemer Venture Partners, Shine Capital, and Rho, with participation from Rocketship, J-Ventures, Future Back Ventures by Bain & Company, and Converge.
The funding is being used to expand our Agents Marketplace and scale operations, not headcount.
See how Zenskar automates the full order-to-cash cycle, from contract to cash, without spreadsheets, workarounds, or engineering tickets.*
The Problem We're Solving
Finance teams aren't struggling because they lack AI tools. They're struggling because the systems underneath those tools were built for a simpler world. These systems label real-world complexity as edge cases and force teams into costly, error-prone workarounds: revenue leakage, delayed collections, audit risk, and finance teams buried in grunt work.
Bolting AI onto these broken foundations is as good as a Ferrari engine on a horse-drawn carriage. Zenskar is purpose-built from the ground up: the architecture handles complexity natively, so AI actually works.
It combines foundational flexibility + deterministic calculation + purpose-built AI agents to deliver end-to-end automation across the order-to-cash cycle - without workarounds, without engineering tickets.
The B2B billing and revenue automation market is $2B today, projected to reach $10B by 2030.
What Customers Say
"We're saving 200+ hours/quarter on invoicing and receivables by completely automating our recurring billing."
- Noy Kalansky, Finance Controller, Pontera
"Zenskar automates revenue recognition accurately for our value-based billing: agents reducing manual hours by 70%."
- Matt Barnard, VP Finance, Vertice
"Zenskar’s agents automated 90% of our billing, integrated with our CRM, and accelerated revenue collection by a month."
- Ming Lui, VP Finance, Yembo
"Sardine had spent 4 years running billing in-house for high-volume, usage-based pricing. Zenskar took care of it all."
"We launched our product 4 months faster instead of building an in-house system for our usage-based pricing."
- Kshitij Gupta, CEO, 100ms
About the role
Zenskar is building the operational backbone for how B2B enterprises run their business. As a Tech Lead on the backend team, you own the work itself, in a domain where correctness is non-negotiable, scale is unforgiving, and the edge cases never stop coming.
You're accountable for whether the solution is right, whether it's correct, and whether it's still right in 18 to 24 months. This role fails when a design leaks money quietly, not loudly.
This is hands-on. You write and review code daily, alongside pairing on hard problems and mentoring the Senior Engineers around you. You have no direct reports, your leverage comes from making the engineers around you better and from decisions that hold up without you in the room, not from headcount. This is not a feature-factory role: you're here to raise the level of abstraction the team works at, not to ship tickets faster.
What you'll walk into
We're Python and Postgres on AWS, running a multi-tenant platform. Problems you'll likely be working on in your first few months:
- Re-architecting inter-service communication into an event-driven system, from scratch.
- Scaling our task management system to handle 10x traffic growth by year-end.
- Scaling our usage ingestion event pipeline from 10k events/sec to 1M events/sec within the year.
What you'll do
- Turn an ambiguous ask like "the customer wants milestone-based pricing" into interfaces, edge cases, failure modes, and a real migration path
- Design for money correctness by default: idempotency, reconciliation, rounding, proration, audit trail, replay. Assume the system will be wrong somewhere and build in how you'd detect it
- Name and quantify technical debt, and negotiate real bandwidth to address it, rather than silently absorbing it or refactoring unilaterally
- Make the engineers around you measurably better: pair on hard problems, review their work, and be able to point to someone who grew because of you
- Push back on a bad technical decision made by someone more senior than you, when you're right, and make it stick
- Stay hands-on: write and review code at a high bar, day to day, not just in review comments
- Work design and product into your technical plans as you build them, not around them after the fact
- Own production debugging end to end: diagnose across services using logs, traces, and metrics, and make incidents boring instead of fires
- Set architectural direction for your domain: the frameworks, patterns, and abstractions the team builds on for years
- Hold a technical bar in interview loops, and shape who joins the team, without owning the headcount decision
- Stay current on the industry through real people and sources, and be able to name a decision of yours that changed because of something you learned recently
Who you are
- 5 to 8 years of professional software development experience building production systems at real companies
- CS degree or equivalent, with strong fundamentals in data structures, algorithms, OS, and networking
(Our interview process is built to test everything above through real evidence - debugging, design under ambiguity, project depth, uplifting others, and reversing bad calls - rather than asking you to restate it here.)
Good to have
- Hands-on observability: comfortable diagnosing complex issues across distributed services using logs, traces, and performance metrics, not just setting up dashboards
- Security and compliance awareness (SOC 2, DPDPA, or similar) with a grasp of the engineering implications
- Experience with event-driven architectures: Kafka, NATS, or RabbitMQ in production
- Cloud platform experience (AWS preferred), deploying and operating services at scale
- Early-stage startup experience: comfortable with ambiguity, evolving requirements, wearing multiple hats
- Background in multi-tenant enterprise software: external integrations, enterprise workflows
- Strong testing culture: unit, integration, and e2e as a default, not an afterthought
- Containerization and CI/CD: Docker, Kubernetes, GitHub Actions
- Uses AI coding tools like Cursor or GitHub Copilot to move faster without compromising quality
- Experience with financial systems, billing platforms, or fintech applications
What drives you
- You can go deep on the hard problems you've solved: the why, the tradeoffs, the outcome
- You ask "what happens when this fails?" before shipping anything
- You own problems, not tasks: you follow through until the system is healthy, not just until the PR is merged
- You set direction without taking over: you make the engineers around you better instead of becoming the single point of knowledge
- You hold strong opinions on system design loosely, and change your mind when the evidence does
- You thrive where requirements evolve and the answers aren't always obvious
- Not taking yourself too seriously :)
Location
Interview process
Our process is structured and evidence-based. No single round exceeds 60 minutes, and every round is scored against a defined set of capabilities rather than general impressions.
- R0, Recruiter screen (15 to 30 min): Fit, motivation, and a quick check for real, specific experience on cross-functional work, uplifting others, technical debt ownership, staying current, and hiring influence.
- R1, Technical: debugging & correctness (60 min): A small billing-shaped code sample with seeded defects. You walk through it, find what's wrong, including the one nobody would notice for a quarter, and fix the one that matters most, live.
- R2, Technical: design under ambiguity (60 min): A deliberately underspecified, Zenskar-shaped problem. You design it, defend your choices, and reason through a real, resolved Zenskar architecture decision.
- R3, Project deep-dive (60 min): A deep dive into one real project you led, the problem, the non-engineering friction, how you knew it was healthy, and how it shipped.
- R4, Founder / Managerial (60 min): Whether someone is measurably better because of you, whether you've reversed a bad call made above you, whether you've held a hiring bar without owning the decision, and overall fit.
- Reference checks: Two former direct managers, contacted by the hiring manager, including who on your team got better because of you and how they'd know.
We'll share detailed feedback and next steps promptly after each round.
