AI Engineer
Role Overview
Construct agentic AI architecture leveraging large language models to orchestrate planning, execution, and tool interactions with minimal supervision.
Work within a compact engineering team to build core foundational infrastructure spanning microservices, integration protocols, state management, tracing, and eval harnesses.
Ensure systems are production-ready, reliable, and scalable.
Hands-on backend software engineering position focused primarily on coding, testing, and system diagnostics.
Prompt design is incorporated into the workflow, but complex architectural challenges reside within the underlying infrastructure.
Welcomes applicants across diverse seniority levels; candidate capability determines final level placement.
Junior engineers pair directly with senior mentors to own production components within three months; senior engineers drive technical standards and architectural vision.
Key Skills for This Role
Full Job Posting
The Job in a Nutshell
Construct agentic AI architecture leveraging large language models to orchestrate planning, execution, and tool interactions with minimal supervision.
Work within a compact engineering team to build core foundational infrastructure spanning microservices, integration protocols, state management, tracing, and eval harnesses.
Ensure systems are production-ready, reliable, and scalable.
Hands-on backend software engineering position focused primarily on coding, testing, and system diagnostics.
Prompt design is incorporated into the workflow, but complex architectural challenges reside within the underlying infrastructure.
Welcomes applicants across diverse seniority levels; candidate capability determines final level placement.
Junior engineers pair directly with senior mentors to own production components within three months; senior engineers drive technical standards and architectural vision.
What You Will Do
- Architect and maintain agent infrastructure, including orchestration logic, session handling, state management, and reliable backend service interfaces.
- Design robust tool integrations and MCP servers featuring 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, 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.
What Are We Looking For
- Deep software engineering expertise in a modern programming language, prioritizing algorithmic depth over superficial multi-language breadth.
- Strong computer science foundations in data structures, algorithms, memory allocation, and concurrent execution patterns.
- Practical experience with backend infrastructure: REST/gRPC APIs, relational/non-relational databases, caching tiers, message queues, and distributed systems resilience.
- Proficiency with Git, automated testing frameworks, CI/CD pipelines, and writing clean, maintainable software.
- Comprehensive mental model of LLM mechanics: context window constraints, tokenization, function calling protocols, non-deterministic behaviors, and hallucination management.
- Demonstrated history of delivering full-lifecycle technical projects across production environments, academic work, open source, or side initiatives.
- Methodical debugging skills with the ability to articulate systematic troubleshooting methodologies.
- Strong technical documentation and written communication skills to articulate complex distributed system behaviors.
Expectations by Seniority Level
- Early Career: Focuses on technical foundations, algorithmic problem-solving, and curiosity; prior agentic experience is not required and mentorship is provided.
- Mid-Level: Owns sub-systems end-to-end, translates ambiguous requirements into robust architecture, and demonstrates a proven track record of shipping and maintaining production services.
- Senior Level: Drives strategic architectural direction, possesses hands-on experience scaling non-deterministic systems, elevates team-wide engineering practices, and actively challenges technical assumptions.
Nice to Have
- Hands-on experience developing application prototypes integrated with LLM APIs.
- Familiarity with Model Context Protocol (MCP) or custom tool integrations.
- Understanding of vector database indexing, embedding generation, and retrieval pipelines (RAG).
- Experience with cloud infrastructure (AWS preferred), containerization (Docker/Kubernetes), telemetry, or IaC tooling.
- Knowledge of GenAI security paradigms, prompt injection mitigation, and threat modeling.
- Active open-source contributions or community engagement.
Out of Scope
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.
Technical Standards & Architecture
Model Context Protocol (MCP): 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.
What We Offer You
- Inclusive and diverse culture that encourages innovation and flexibility in-office, and hybrid work setups.
- Highly competitive compensation packages, including bonuses and the potential for shares.
- Personal development with regular training and an annual learning stipend.
- Join a talented team of over 30 nationalities working in 14 countries.
- Autonomy, mentoring, and challenging goals.
Who Are We
Foodics is a leading restaurant management ecosystem and payment tech provider founded in 2014 with headquarters in Riyadh and offices across 5 countries.
Serving customers and partners in over 35 different countries worldwide.
Products have processed over 6 billion orders.
Achieved three rounds of funding, with the latest raising $170 million in the largest SaaS funding round in MENA.
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