Own and build Zalos' core agent architecture and production-grade LLM pipelines to turn natural-language intents into reliable, auditable cross-system workflows. Implement context management, tool/function calling, failure handling, and tests; collaborate with founders and customers to improve AI reliability in enterprise systems.
About the role
What we're building
What you'll work on
Must haves
Nice to haves
The team you'll work with
Why Zalos
About the interview
As Zalos's Founding Full-Stack Engineer, your job is to build the product experience that makes this real. You'll be designing and shipping the application surfaces, backend services, and workflow infrastructure that turn messy enterprise work into correct, auditable action across systems of record. This is a hard, important part of the product. It's entirely yours to own.
Enterprise software was built to store data and enforce process. It was never built to be used by humans who have better things to do. The result: highly trained people wasting hours every day fighting screens instead of doing the actual work.
Zalos attacks this from three angles:
- Intent-to-action: A user describes an outcome. Zalos figures out which systems to touch, in which order, with which data, and executes it with the right approvals and a clean audit trail.
- Cross-system workflows: Event-driven chains that span SAP, Salesforce, and ServiceNow simultaneously.
- Computer-use for the long tail: Not every enterprise workflow has a reliable API. We use computer-use agents to automate the 30 to 40 percent of processes that live in screens, VDI sessions, and legacy thick clients.
- Design and own the core agent architecture: how we take a natural language intent, decompose it into steps, call the right APIs and UI agents, handle failures gracefully, and close the loop with the user
- Build reliable, production-grade LLM pipelines covering context management, tool use, structured outputs, and multi-step reasoning across systems with real enterprise data
- Work directly with the founders and customers to understand where AI fails in real enterprise workflows and translate that into better systems
- Set the technical standards for how we build AI at Zalos
- You've shipped AI-powered features that real users depend on in production. You know what breaks and how to prevent it
- Deep hands-on experience with LLM APIs, agentic patterns, and tool/function calling
- You think about reliability, latency, and cost as first-class engineering concerns, not afterthoughts
- You build tests/evals before you ship, not after something breaks in front of a customer
- High ownership: you take something from an ambiguous problem statement to a clean, working solution with minimal hand-holding
- You're pragmatic. Startups are always on fire somewhere. You know when to let something burn and when to drop everything
- Computer-use or browser automation work with tools like Playwright, browser agents, or UI parsing
- Fine-tuning or RLHF on domain-specific tasks
- Prior founding engineer or early-stage startup experience
You'll work directly with the founders from day one. No engineering manager in the way, no sprint planning theatre.
- The market is real and enormous. The system integration market alone is $380B.
- You'll work on genuinely hard AI problems. Reliable agents over messy enterprise systems, computer-use for legacy UIs, cross-system orchestration with audit trails.
- Paying customers from day one. We're not pre-product. You'll see your work matter in real enterprise workflows within weeks of joining.
- Small team, high trust. You'll have a direct line to founders and customers. Your opinions shape the product.
We move fast and respect your time. No leetcode. No whiteboard puzzles.
1. 15-min intro call with a founder. We'll walk you through what we're building and what we need. You'll tell us what you've shipped.
2. Take-home technical task. No trick questions. We want to see how you think, not how you memorize.
3. 1-hour technical deep-dive with our CTO. Walk through your design, discuss tradeoffs, get into the weeds on production reliability.
4. 3-day paid work trial. Build something real with us. This is the most predictive step we have.
5. Offer. If we're aligned, you'll have an offer within a week of the trial.
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