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.