Associate AI Quality Engineer

About Foodics

Foodics is a leading restaurant management ecosystem and payment technology provider, established in 2014. Headquartered in Riyadh, the company operates across 5 countries and serves customers and partners in over 35 countries worldwide, having processed more than 6 billion orders. Foodics has successfully completed three funding rounds, with the most recent raising $170 million, marking the largest SaaS funding round in MENA.

Role Overview

Foodics is seeking an Associate AI Quality Engineer with 0-1 years of experience for a full-time position. This role focuses on developing internal AI systems for engineers, including agents for test generation and maintenance, pipelines for failure triage, tooling for code review and debugging, and evaluation infrastructure for AI features. The successful candidate will write production code, manage systems in CI, and be evaluated based on the adoption of their work by other engineers.

Key Responsibilities

• Implement test automation across the stack, covering backend (API, contract, service-level, integration, data setup, schema-adaptive test design), frontend (Web E2E, component-level with Playwright, visual and RTL regression, fast suites for merge gating), and mobile (native and cross-platform with Appium or Maestro, device-farm strategy, offline/sync behavior, payment-peripheral paths).
• Develop connective tissue for testing, including shared fixtures, environment and test-data management, parallelization, and CI pipelines.
• Design and implement the AI layer for quality, focusing on test generation from specs, code, and production traffic, with an emphasis on maintenance.
• Develop failure triage systems to classify red builds (real bug, flake, environment, test rot) before human intervention.
• Create self-healing locators and suite health tooling, including flake detection, quarantine, and coverage-gap analysis.
• Build evaluation infrastructure for AI features across products, encompassing datasets, scoring, and regression detection for prompt or model changes.
• Implement evaluation specific to market needs, including Arabic and English behavior, RTL interfaces, and region-specific POS, tax, and payment rules.
• Develop and orchestrate agentic AI for tasks such as reading diffs, running relevant suites, reproducing failures, proposing fixes, and opening PRs.
• Design agents to own end-to-end quality workflows, including exploratory testing, coverage-gap hunting, release-risk assessment, and escalation.
• Integrate AI systems with existing stack components such as CI, Jira, observability, and MCP-style tool interfaces.

Required Technical Grounding

Candidates should possess current knowledge and critical understanding of:

• Test automation: Framework design and layering, test pyramid, flake economics, parallel execution, mobile and cross-browser realities, CI/CD gating.
• Frameworks: 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 cost considerations.
• Context engineering: Retrieval strategy, chunking, reranking, caching, and managing long-context behavior.
• Evaluation: Offline and online evaluations, 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 critical systems.
• Operations: Tracing and observability for LLM systems, prompt and version management, latency and cost budgeting, model routing, and when fine-tuning or distillation is preferable to a prompt change.

Candidate Qualifications

• An engineer who ships production software, with recent hands-on experience on LLM-backed systems used by real users.
• Strong Python skills; comfortable in at least one of .NET, Java, or TypeScript.
• Experience with tested, maintained code, not solely notebooks.
• Demonstrated automation depth across more than one surface, having owned a suite that gates releases on both backend and a UI (web or mobile), with the ability to maintain its integrity.
• Practical experience building evaluation systems, with the ability to quantify system improvements.
• Practical depth with the modern LLM toolkit (prompting, structured output, tool use, retrieval, agentic AI orchestration) and a clear understanding of trade-offs.
• Credible testing fundamentals, including familiarity with test design, automation frameworks, and CI/CD.
• A focus on adoption, measuring work by its use by other engineers.

Benefits and Culture

Foodics offers an inclusive and diverse culture that supports innovation and flexible in-office and hybrid work arrangements. The company provides competitive compensation packages, including bonuses and potential for shares. Personal development is prioritized through regular training and an annual learning stipend. This role offers the opportunity to join a diverse team of over 30 nationalities working in 14 countries, with autonomy, mentoring, and challenging goals.