Associate AI Quality Engineer

About Foodics

The Role

Test Automation

• Build backend API, contract, service-level, and integration test coverage.
• Build frontend web end-to-end, component, visual, and RTL regression coverage with fast merge-gating suites.
• Build mobile coverage with Appium or Maestro, including device farms, offline and synchronization behavior, and payment-peripheral flows.
• Manage shared fixtures, environments, test data, parallelization, and meaningful CI pipelines.
• Include performance and load testing in the quality approach.

AI Quality Systems

• Generate tests from specifications, code, and production traffic while addressing ongoing maintenance.
• Classify failed builds as real bugs, flaky tests, environment issues, or test rot before human review.
• Build self-healing locators, flake detection, quarantine, suite-health, and coverage-gap tooling.
• Create AI evaluation infrastructure with datasets, scoring, and regression detection.
• Evaluate Arabic and English behavior, RTL interfaces, and region-specific point-of-sale, tax, and payment rules.

Agentic AI and Orchestration

• Build agents that read diffs, run relevant suites, reproduce failures, propose fixes, and open pull requests.
• Develop end-to-end quality workflows for exploratory testing, coverage-gap discovery, and release-risk assessment.
• Implement planning, tool use, retries, state, memory, sandboxed execution, and multi-agent handoffs.
• Integrate with CI, Jira, observability systems, and MCP-style tool interfaces.
• Choose deterministic pipelines over agents when they are the more effective solution.

Technical Ground

• Apply test automation architecture, test layering, test-pyramid concepts, flake economics, parallel execution, mobile and cross-browser testing, and CI/CD gating.
• Apply agentic AI orchestration, multi-step planning, memory, state, sandboxing, multi-agent patterns, and tool-integration standards.
• Apply retrieval, chunking, reranking, caching, and long-context management in context engineering.
• Apply offline and online evaluation, LLM-as-judge techniques, human review, statistical significance, and CI regression gates.
• Apply structured output, guardrails, fallback and retry design, and non-determinism handling.
• Apply LLM tracing, observability, prompt and version management, latency and cost budgeting, model routing, fine-tuning, and distillation.

What We Are Looking For

• Production software engineering experience with recent hands-on work on LLM-backed systems used by real users.
• Strong Python skills and comfort with at least one of .NET, Java, or TypeScript.
• Automation ownership across backend and at least one user-interface surface, such as web or mobile.
• Experience building evaluation systems and measuring improvement with quantitative results.
• Practical knowledge of prompting, structured output, tool use, retrieval, and agentic AI orchestration.
• Strong testing fundamentals, including test design, automation frameworks, and CI/CD.
• A focus on adoption and measuring success by whether other engineers use delivered systems.

What Foodics Offers

• An inclusive and diverse culture with in-office and hybrid work setups.
• Competitive compensation packages, including bonuses and potential shares.
• Professional development, regular training, and an annual learning stipend.
• A multinational team, autonomy, mentoring, and challenging growth opportunities.