About the role
As a Forward Deployed AI Engineer, you will be embedded with customer teams to take ownership of engagements, guiding them from initial discussions through to deployment in production environments. Your contributions will deliver immediate value while allowing you to iterate on systems, ensuring the impact of your work grows over time.
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Key Responsibilities
- Embed: Collaborate on-site with operators and executives, managing each engagement as an internal venture, ensuring alignment with customer needs.
- Scope: Translate ambiguous, high-stakes challenges into precise technical plans, prioritizing tasks that deliver maximum value.
- Build: Design and deploy high-quality, scalable AI systems that leverage Large Language Models (LLMs).
- Drive adoption: Implement systems effectively, measure user outcomes, and iterate as needed to ensure impact.
- Shape direction: Support enterprise teams in AI adoption and steer the technical direction of each project.
Technical Requirements
- Engineering fundamentals: Proficient in Python or TypeScript, with experience in developing reliable production systems using contemporary DevOps practices.
- AI-native: Familiarity with modern LLM tooling (e.g., LangChain, LlamaIndex, Guardrails, MCP), and a comprehensive understanding of AI system evaluation, debugging, and observability.
- Building AI systems: Over 1 year of experience in building and deploying LLM-powered applications or AI agents in production, including retrieval-augmented generation (RAG) pipelines, tool use, and multi-step AI workflows.
Core Values
- Customer first: Focus on measurable outcomes and prioritize customer needs.
- Ownership mentality: Take end-to-end responsibility for all outcomes and proactively address issues.
- Clear communicator: Simplify complex systems for various stakeholders, ensuring the visibility of your work’s value.
- Comfortable with ambiguity: Excel in environments with incomplete information and enjoy tackling undefined problems.
- Long-term builder: Contribute to the organization’s success by integrating field insights into strategy and operations.
Work Environment
- Location: Riyadh, Saudi Arabia
- Compensation Package: Base salary supplemented with allowances for housing, transportation, and education, along with medical coverage and participation in an employee equity program. Specific terms are provided at the offer stage.
Work Approach
- Curiosity: Investigate how work is conducted on-site and continuously learn as technology evolves.
- Speed: Quickly transform briefs into functional code without waiting for ideal conditions.
- Rigour: Test and validate your code, ensuring that you can rationalize every decision made.
- Confidence & humility: Engage in constructive challenges and remain open to new evidence that informs your perspective.
- Autonomy: Make informed decisions in uncertain situations and manage your own workload.
- Resourcefulness: Navigate challenges with limited resources and unfamiliar systems effectively.
- Situational leadership: Adapt your leadership approach based on context, leading or following as the situation demands.
- A force multiplier: Ensure that your documentation, patterns, and reviews enhance the productivity of the entire team.
- AI-native depth: Integrate AI tools into your coding practices and have tangible experience building LLM-based solutions.
- Experience Level: Open to candidates ranging from entry-level roles to those with several years of deployment experience, particularly fluent in Python, TypeScript, or similar languages.
