Staff Engineer (GenAI)

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 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.

Read the announcement →

https://youtu.be/jRb2ybvrSwU?si=vuLLgUY_MFdRzDMl

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

About the role

This role sets the standard for how Gen AI gets built into Zenskar's product.

You're accountable for whether the Gen AI features Zenskar ships are reliable, safe, and cost-sane in production, not for whether any single feature gets built. 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 an IC role with org-wide influence, not team ownership. You'll code on the critical paths where it matters most, but your leverage comes from setting patterns other teams build on top of, not from personally shipping every Gen AI feature. Think 1 to 3 year horizon for how Gen AI gets integrated across the product, not one team's roadmap.

What this role is not: foundation model training, deep RAG/retrieval specialization as a primary mandate, fine-tuning specialization, or MLOps infrastructure ownership. Those are adjacent lanes. 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. Design the verification or human-in-loop layer wherever money is actually at stake
• Pick the right model or vendor for the job (cost, quality, latency, data policy) and avoid lock-in. Once chosen, run it efficiently: token economics, caching, batching, per-request cost tracking
• Set the technical standard other teams build their Gen AI features against, and get multiple teams to actually adopt it without having direct authority over them
• Build and own the eval harness that catches quality regressions before they ship, not ad hoc spot-checks
• Know when not to use an LLM at all, where a deterministic system would be cheaper, more reliable, or simply correct
• Name and quantify technical debt in Gen AI systems (prompt sprawl, eval gaps, untracked cost, brittle integrations) and negotiate time to address it
• Shape who joins the team by holding a technical bar in interview loops, without owning headcount decisions

What drives you

• You've seen AI systems fail in production and got obsessed with understanding why
• You think about reliability, evaluation, and observability before you think about model selection
• You believe probabilistic systems deserve the same engineering rigor as deterministic ones
• You care about systems users can trust, not systems that look impressive in a demo
• Gen AI's baseline moves monthly for you, not yearly. You actively track it: specific people, sources, releases, and can point to a real decision that changed because of a recent shift

What you'll need

• 8+ years of software engineering experience, including meaningful experience shipping AI-powered products to production
• A real, shipped-at-scale system in your track record, doesn't have to be Gen AI. If it's ML work, it shipped to real users, not fine-tuning or research that stayed in a notebook
• Deep understanding of LLM application architecture: tool use, structured outputs, retrieval, orchestration, and where these actually break in production
• Experience building agentic systems that do multi-step reasoning and interact with tools or business systems
• A track record of treating prompts as versioned, testable, deployable artifacts, not strings scattered through the codebase
• Strong RAG fundamentals: retrieval quality, chunking, embeddings, evaluation, knowledge system design
• Experience designing evaluation frameworks and regression pipelines for AI systems
• Strong grasp of AI observability: tracing, monitoring, feedback loops, production debugging
• A track record of getting more than one team to adopt a pattern or standard you set, without owning those teams
• Judgment for when not to use an LLM at all
• Strong backend engineering skills, and enough frontend ability to own an AI experience end to end
• Can clearly walk through an AI system you've built two levels below the pitch: what failed, what you learned, how you improved reliability over time

Good to have

• Memory architectures: episodic memory, procedural memory, retrieval systems, knowledge stores
• Agent orchestration frameworks (LangGraph, Pydantic AI, OpenAI Agents SDK, CrewAI, or a custom runtime)
• Long-running autonomous workflows and event-driven agent systems
• Voice, multimodal, or real-time AI systems
• Fine-tuning experience and understanding of model internals beyond API consumption
• Open-source model deployment and inference infrastructure
• Experience with financial systems, billing platforms, revenue operations, accounting, or fintech
• Familiarity with MCP, tool ecosystems, and AI platform architecture
• Startup experience and comfort operating with high ownership and no formal authority

Location

• Hybrid - 2 days per week
• Office Location: Indiranagar, Bengaluru.
• Address: 3rd Floor, A wing No 1, Carlton Towers, HAL Old Airport Rd, HAL 2nd Stage, Indiranagar, Bengaluru, Karnataka 560008.

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 (30 min): Fit, motivation, and a quick check that you have real, specific experience behind your background, not generic answers.
• R1, Technical: critique a broken Gen AI integration (60 min): You'll be handed a small billing-shaped Gen AI integration with seeded defects and asked to find them, including the one that would go unnoticed until it was already wrong on a customer's invoice.
• R2, Technical: design a new integration and its eval framework (60 min): An intentionally underspecified product ask. You design the integration, decide whether an LLM belongs in it at all, choose a model with real reasoning, and design how you'd know it's working before a customer complains.
• R3, Project audit (60 min): A numbers-anchored walkthrough of one real system you've shipped at real scale, architecture, usage, cost, and the debt it accumulated.
• R4, Architect and influence (60 min): Whether you've actually set a technical pattern that more than one team adopted, and how you've held a hiring bar without owning headcount.
• R5, Bar raiser (60 min): Values, judgment, and a deeper probe on whatever came through thinnest earlier in the process.
• Reference checks: Two former colleagues, run by two different people on our side, including a deep dive on a Gen AI system you shipped and what happened after it hit production.

We'll share detailed feedback and next steps promptly after each round.

How to apply

Interested? Click here to apply → https://evolve.keka.com/careers/applyjob/64477