Senior AI Backend Engineer - Agent Evaluation & Quality

Salla

1d

About The Position

We 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.

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 Haves

• Direct 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.

Responsibilities

• Own 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed