About the Role
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, while also writing production code yourself. This role requires you to balance two responsibilities: as an AI expert and as a leader.
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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 transition key work from experimentation to production.
- Introduce leading testing frameworks for Agentic development.
- Ensure production monitoring and online evaluations 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, raising the technical bar through pairing and feedback, and contribute to hiring processes.
Hands-On Delivery
- Lead debugging of critical production incidents involving AI behavior.
- Manage the building of prototypes/experiments to validate new capabilities (new models, agent patterns, voice features) before team commitment.
Cross-Functional Partnership
- Work with Product to convert customer use cases into technical requirements.
- Collaborate with Engineering on shared platform work: tool calling, workflows, deployment, and scaling.
Mindset and Behaviors
- Ownership: Takes outcomes end-to-end, bridging gaps between teams and following through without being asked.
- Trade-off Judgement: Grounds decisions in business needs, clearly explaining trade-offs between quality, speed, and cost.
- Constructive Challenge: Comfortable questioning existing approaches and proposing better alternatives.
- Team Player: Shares credit, assists teammates in succeeding, and prioritizes team goals over individual victories.
- Creativity and Innovation: Solves complex challenges with 'out-of-the-box' thinking and champions innovation.
Requirements
- Experience: 7+ years in software or ML engineering, including 2+ years building LLM-based applications in production. Experience in AI platform-level development is necessary, beyond just point solutions.
- Technical Leadership: Proven experience leading a team of 3+ AI engineers/scientists technically, including design reviews, standards, and mentoring.
- LLM Application Engineering: Deep hands-on experience with prompting, structured outputs, guardrails, and agent patterns/harness development (planning, tool use, multi-step reasoning).
- Ability to Work in Ambiguous Settings: Proven capability to bring clarity and structure to complex situations.
- Evaluation: Experience building LLM evaluation pipelines (golden datasets, LLM-as-judge, regression suites) and using them to gate releases.
- Production Engineering: Strong proficiency in Python, with experience in APIs, cloud deployment, observability, and debugging distributed systems.
- Communication: Fluent written and spoken English, capable of producing clear design documentation 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 optimization.
- Production Monitoring for LLMs: Hands-on experience with tracing and online evaluation tools (e.g., Langfuse, Arize, or similar).
- Model Adaptation: Experience fine-tuning or distilling open-weight models and serving them cost-effectively.
