AI Tech Lead

Role summary

The AI Tech Lead is the senior hands-on engineer accountable for the quality, reliability and technical direction of the AI layer of our agentic platform. You will set the technical bar for a team of AI engineers who each own an area, and you will write production code yourself. You will need to wear 2 hats, one is the AI hat and the other is leadership

Key responsibilities

Technical direction

• Own and be accountable for the AI architecture and harness: model selection, prompting patterns, retrieval design, guardrails and voice pipeline.
• Make and document technical decisions with clear rationale.

Quality, evaluation and reliability

• Define the evaluation standard and move key work from experimentation to production
• Introduce leading testing frameworks for Agentic development
• Ensure production monitoring and online evals catch quality drops, hallucinations and latency spikes before customers report them.

Team leadership and engineering practices

• Lead the AI team in designing, developing, and delivering our Agentic Platform
• Mentor AI engineers, raise the technical bar through pairing and feedback, and contribute to hiring.

Hands-on delivery

• Lead debugging of critical production incidents involving AI behaviour.
• Manage the building of prototypes/experiments to validate new capabilities (new models, agent patterns, voice features) before the team commits to them.

Cross-functional partnership

• Work with Product to turn customer use cases into technical requirements
• Work with Engineering on shared platform work: tool calling, workflows, deployment and scaling.

Mindset and Behaviors

• Ownership: takes outcomes end to end, including gaps between teams, and follows through without being asked.

• Trade-off judgement: grounds decisions in business needs and can explain the trade-offs between quality, speed and cost.

• Constructive challenge: comfortable questioning existing approaches and proposing better ones.

• Team player: shares credit, helps teammates succeed, and puts team goals ahead of individual wins.

• Creativity and innovation: solve complex challenges with 'out-of-box' thinking and be a champion for innovation

• Experience: 7+ years in software or ML engineering, including 2+ years building LLM-based applications in production. Not just building AI point solutions, AI platform level experience is required.

• Technical leadership: has led a team of 3+ AI engineers/scientist technically (design reviews, standards, mentoring)

• LLM application engineering: deep hands-on experience with prompting, structured outputs, guardrails, agent patterns/harness development (planning, tool use, multi-step reasoning).

• Ability to work in ambiguous settings: can bring clarity and structure to complex situations

• Evaluation: has built LLM evaluation pipelines (golden datasets, LLM-as-judge, regression suites) and used them to gate releases.

• Production engineering: strong Python; experience with APIs, cloud deployment, observability and debugging distributed systems.

• Communication: fluent written and spoken English; able to write clear design docs and decision records.

Nice-to-have qualifications

• Arabic NLP: experience with Arabic and its dialects, especially Gulf Arabic, in LLM or speech systems.
• Voice AI: experience with ASR, TTS and real-time voice agents, including latency optimisation.
• Production monitoring for LLMs: hands-on use of tracing and online eval tools (for example Langfuse, Arize or similar).
• Model adaptation: fine-tuning or distilling open-weight models, and serving them cost-effectively.