Senior AI Quality Engineer

Foodics

About The Position

Foodics is a leading restaurant management ecosystem and payment tech provider, founded in 2014 with headquarters in Riyadh and offices across 5 countries. We serve customers in over 35 countries and have processed over 6 billion orders. Foodics is one of the fastest-evolving SaaS companies in the MENA region, having secured significant funding to boost innovation. We are seeking a highly skilled Senior Automation Testing Engineer to inspect product updates before launch. This role involves overseeing the quality assurance process, collaborating with development teams, defining tests for functionality and performance, developing test frameworks, and ensuring products meet high-quality standards.

Requirements

Recent hands-on work on LLM-backed systems that real users depend on.
Strong Python; comfortable in at least one of .NET, Java, or TypeScript.
Tested, maintained code — not notebooks.
Real automation depth across more than one surface.
Experience owning a suite that gates releases on backend and on a UI — web or mobile.
Real experience building evaluation systems.
Practical depth with the modern LLM toolkit — prompting, structured output, tool use, retrieval, agentic AI orchestration.
Credible testing fundamentals (test design, automation frameworks, CI/CD).
A bias toward adoption; measuring work by what other engineers use.

Nice To Haves

Framework: Playwright, Appium, Maestro
Agentic AI: orchestration and tool use, multi-step planning, memory and state, sandboxed execution, multi-agent patterns, MCP and similar tool-integration standards, and the cost of each.
Context engineering: retrieval strategy, chunking, reranking, caching, and managing long-context behaviour.
Evaluation: offline and online evals, LLM-as-judge and its failure modes, human-in-the-loop review, statistical significance on small samples, regression gates in CI.
Reliability: structured output, guardrails, fallback and retry design, and handling non-determinism in systems that must not flap.
Operations: tracing and observability for LLM systems, prompt and version management, latency and cost budgeting, model routing, and when fine-tuning or distillation beats a better prompt.

Responsibilities

Test automation across the stack (Backend, Frontend, Mobile)
Backend: API and contract testing, service-level and integration coverage, data setup, test design.
Frontend: web E2E and component-level coverage with Playwright, visual and RTL regression.
Mobile: native and cross-platform coverage with Appium or Maestro, device-farm strategy, offline and sync behavior, payment-peripheral paths.
The connective tissue: shared fixtures, environment and test-data management, parallelisation, and CI pipelines.
Performance and load testing.
Test generation from specs, code, and production traffic.
Failure triage that classifies a red build before a human opens it.
Self-healing locators and suite health tooling.
Evaluation infrastructure for AI features across our products.
Evaluation for our market specifically (Arabic and English behavior, RTL interfaces, region-specific POS, tax, and payment rules).
Agentic AI that does real work in our pipelines: reads a diff, runs the relevant suite, reproduces a failure, proposes a fix, opens the PR.
Agents that own a quality workflow end to end.
Orchestration that holds up under load.
Integration with existing CI, Jira, and observability systems.