✨ AI Summary
This role is for a Senior AI Backend Engineer focused on Agent Evaluation & Quality. The primary responsibility is to build and own the evaluation systems for production multi-agent systems, ensuring agent performance, catching regressions, and maintaining quality. This involves designing and building LLM-as-judge systems, calibrating them against human labels, and making agent quality measurable. You will also build per-PR eval harnesses, regression detection wired into CI, and user simulators. Opportunities to contribute to agent development itself will also arise, leveraging your deep understanding of failure modes.
Key requirements include strong software engineering fundamentals (Python/Typescript, API/system design, testing, CI/CD), hands-on LLM/agent experience (agents, RAG, tool/function calling, orchestration frameworks), a measurement mindset (metrics, calibration, experiments), and production experience with LLM systems (reliability, latency, cost, observability). A minimum of 5 years of software engineering experience with recent hands-on LLM/agent work is mandatory. Nice-to-haves include direct experience evaluating LLM/agent systems, familiarity with observability tooling, Arabic language/NLP experience, and e-commerce or merchant-facing product experience.
Requirements
Strong software engineering fundamentals. Production Python or Typescript (or similar), clean API and system design, testing, CI/CD. You write code others build on - evaluation infrastructure is real engineering.Hands-on LLM/agent experience. You've built with LLMs - agents, RAG, tool/function calling, orchestration frameworks (LangGraph, LangChain, or equivalent) - and understand how they behave and break.A measurement mindset. You reason about metrics, calibration, and experiments; you want to quantify whether something works, not just ship it.Production experience. You've run LLM systems in production and dealt with reliability, latency, cost, and observability.5+ years software engineering, with recent hands-on LLM/agent work.Nice to haveDirect experience evaluating LLM/agent systems - offline/online eval, LLM-as-judge, systematic regression testing.Observability tooling (Arize, LangSmith, or similar).Arabic language / NLP experience.E-commerce or merchant-facing product experience.
Description
About the roleWe run production multi-agent systems that handle real work for a large base of users. As those systems grow, our biggest constraint is confidence: we need to know how well the agents perform, catch regressions before they ship, and keep quality steady as we release. This role owns that.You'll build the evaluation systems behind our agents - the judges, test harnesses, and simulators that tell us whether an agent is working and where it's failing. The goal is to let us ship agents faster because we can trust what the evaluation tells us.Evaluation is the focus, but it won't be the boundary. Because you'll understand the agents' failure modes better than anyone there will also be opportunities to contribute to agent development itself, building and improving the agents alongside the systems that evaluate them.ResponsibilitiesOwn the evaluation stack. Design and build LLM-as-judge systems, calibrate them against human labels, and make agent quality measurable per-agent and per-failure-mode.Make the release gate real. Build per-PR eval harnesses and regression detection wired into CI, so quality is enforced automatically, not by manual passes.Build user simulators to generate test coverage and adversarial cases before real users hit them.Turn production signal into improvement - pipe real failures back into evaluation sets so the system compounds over time.Partner with product to turn "what good looks like" into concrete, measurable criteria.Grow into agent development - contribute to building and hardening the agents themselves, starting with the components you know most deeply from evaluating them.