AI Engineer

About Foodics

Foodics, established in 2014, is a restaurant management ecosystem and payment technology provider headquartered in Riyadh, Saudi Arabia. With offices in five countries, including UAE, Egypt, Jordan, and Kuwait, Foodics serves customers and partners in over 35 countries globally. The company's products have processed over 6 billion orders, positioning Foodics as a rapidly evolving SaaS company in the MENA region. Foodics has secured three rounds of funding, with the latest raising $170 million, enhancing its innovation capabilities to support business owners.

The Role of AI Engineer

Foodics is seeking an AI Engineer to join its team in Riyadh, Riyadh Province. This full-time position involves constructing agentic AI architecture, leveraging large language models to orchestrate planning, execution, and tool interactions with minimal supervision. Working within a compact engineering team, the AI Engineer will build core foundational infrastructure, including microservices, integration protocols, state management, tracing, and evaluation harnesses, ensuring systems are production-ready, reliable, and scalable.

This is a hands-on backend software engineering role focused on coding, testing, and system diagnostics. While prompt design is part of the workflow, complex architectural challenges are primarily within the underlying infrastructure. The company welcomes applicants across diverse seniority levels, with final placement determined by candidate capability, given that production-grade agentic engineering is an emerging discipline. Opportunities are available for early-career developers through veteran software architects, with engineering excellence mandatory at all levels. Junior engineers will pair with senior mentors to own production components within three months, while senior engineers will drive technical standards and architectural vision.

Key Responsibilities

• Architect and maintain agent infrastructure, including orchestration logic, session handling, state management, and reliable backend service interfaces.
• Design robust tool integrations and Model Context Protocol (MCP) servers with precise boundary definitions and comprehensive error recovery handlers.
• Implement end-to-end tracing and observability for model calls, retrieval, and tool executions, adhering to OpenTelemetry GenAI standards.
• Construct evaluation suites, benchmarks, and regression tests to validate model iterations and systematically capture production edge cases.
• Perform deep-dive root cause analysis on runtime failures, loops, context degradation, or hallucinated outputs to strengthen system resiliency.
• Enforce runtime guardrails, cost thresholds, Role-Based Access Control (RBAC) permissions, output validation schemas, and human-in-the-loop validation triggers.
• Monitor industry advancements, emerging AI research, and architectural patterns, sharing technical insights across the engineering organization.

Technical Standards and Architecture

The engineering environment at Foodics adheres to industry standards and operational frameworks, with continuous learning integrated into the engineering onboarding process. Key standards include:

• Model Context Protocol (MCP): An open standard managed via the Linux Foundation for integrating models with tool services and data stores.
• OpenTelemetry GenAI Conventions: Standardized instrumentation schemas for tracking model execution traces, step transitions, and tool invocations.
• OWASP GenAI & Agentic Security Guidelines: Threat modeling standards emphasizing containment strategies, least-privilege tooling, strict input sanitization, and authorization gates for critical actions.
• Eval-Driven Development: Mandatory evaluation validation prior to shipping behavioral changes, relying on internal domain-specific test harnesses rather than external benchmark metrics.

Qualifications and Experience

Candidates should possess strong backend software engineering skills. The following areas are considered out of scope for this role:

• Extensive experience with specific agent frameworks, given the nascent nature of the ecosystem.
• Academic AI research background, specialized ML degrees, or mathematical derivation of backpropagation.
• Deep expertise in base model training or fine-tuning workflows.
• Flawless portfolio presentations; emphasis is placed on technical depth and problem-solving reasoning.
• Strictly conventional career trajectories; non-traditional and self-taught engineering backgrounds are evaluated equally.

Application Process

Interested candidates are encouraged to apply. The company values technical depth and problem-solving reasoning in its evaluation process.