About the Role
The AI Tech Lead is a crucial position responsible for the quality, reliability, and technical direction of the AI layer of our agentic platform. This role requires both hands-on engineering skills and leadership qualities. You will establish the technical standards for a team of AI engineers, each tasked with a specific area, while also contributing to the codebase directly. Balancing technical expertise with leadership responsibilities is essential for success in this role.
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Key Responsibilities
Technical Direction
- Own and manage the AI architecture and harness, which includes model selection, prompting patterns, retrieval design, guardrails, and voice pipeline.
- Make and document technical decisions well-supported by clear rationale.
Quality, Evaluation, and Reliability
- Define the evaluation standards and transition critical work from experimentation into production.
- Introduce leading testing frameworks to ensure high-quality development on the Agentic platform.
- Enable production monitoring and online evaluations to detect quality drops, hallucinations, and latency spikes before customer incidents arise.
Team Leadership and Engineering Practices
- Guide the AI team in the design, development, and delivery of the Agentic Platform.
- Mentor AI engineers by providing feedback and pairing to uplift the technical standards while contributing to the hiring process.
Hands-on Delivery
- Lead the debugging efforts on critical production incidents related to AI behavior.
- Oversee the development of prototypes and experiments to validate new capabilities (new models, agent patterns, voice features) ahead of team commitments.
Cross-Functional Partnership
- Collaborate with Product to translate customer use cases into technical requirements.
- Partner with Engineering on shared platform tasks such as tool calling, workflows, deployment, and scaling.
Mindset and Behaviors
- Ownership: Takes responsibility for outcomes from start to finish and addresses gaps between teams proactively.
- Trade-off Judgement: Bases decisions on business needs, effectively explaining trade-offs between quality, speed, and cost.
- Constructive Challenge: Comfortable questioning existing methodologies and proposing enhancements.
- Team Player: Values teamwork, shares credit, assists teammates in achieving success, and prioritizes team objectives over personal achievements.
- Creativity and Innovation: Tackles complex challenges with innovative thinking and promotes a culture of innovation.
Requirements
- Experience: Minimum of 7 years in software or machine learning engineering, with at least 2 years building LLM-based applications in production. Experience in AI platform-level development is essential, rather than just AI point solutions.
- Technical Leadership: Proven experience leading a technical team of at least three AI engineers/scientists, including design reviews and mentoring.
- LLM Application Engineering: Extensive hands-on experience with prompting, structured outputs, guardrails, and the development of agent patterns/harness involving planning, tool use, and multi-step reasoning.
- Ambiguity Management: Capable of providing clarity and structure in complex, less defined situations.
- Evaluation: Experience in building LLM evaluation pipelines (golden datasets, LLM-as-judge, regression suites) and employing them for release gating.
- Production Engineering: Strong proficiency in Python and experience with APIs, cloud deployment, observability, and debugging distributed systems.
- Communication: Fluency in written and spoken English; ability to produce clear design documentation and decision records.
Nice-to-have Qualifications
- Experience with Arabic NLP, particularly its dialects, especially Gulf Arabic, in LLM or speech systems.
- Familiarity with Voice AI technologies including ASR, TTS, and the optimization of real-time voice agents for latency.
- Practical experience with production monitoring for LLMs, including tools like Langfuse, Arize, or similar.
- Skills in model adaptation such as fine-tuning or distilling open-weight models for effective cost management.
