AI Tech Lead

Lucidya

About The Position

Lucidya builds an operating system for agentic customer experience. Its flagship product line, Agent Studio, is an agentic platform where customers build and run AI agents that address real customer experience issues through text and voice in Arabic and English. The AI Tech Lead is the senior hands-on engineer accountable for the quality, reliability, and technical direction of the AI layer of the agentic platform. This person sets the technical bar for a team of AI engineers, each of whom owns an area, while also contributing production code. The role combines AI engineering and technical leadership. The position owns the AI architecture and harness, including model selection, prompting patterns, retrieval design, guardrails, and the voice pipeline. It defines evaluation and reliability standards, advances work from experimentation to production, and establishes testing, monitoring, and online evaluation practices. The AI Tech Lead leads and mentors the AI team, contributes to hiring, handles critical production debugging, and manages prototypes to validate new capabilities. The role partners with Product to translate customer use cases into technical requirements and with Engineering on shared platform work such as tool calling, workflows, deployment, and scaling. The role calls for an ownership-oriented, collaborative approach, sound judgment around quality, speed, and cost, constructive challenge of existing approaches, and creative problem solving.

Requirements

7+ years of experience in software or ML engineering, including 2+ years building LLM-based applications in production.
Experience with AI platform-level work, not only AI point solutions.
Technical leadership of a team of 3+ AI engineers or scientists, including design reviews, setting standards, and mentoring.
Deep hands-on experience with prompting, structured outputs, guardrails, agent patterns, and harness development, including planning, tool use, and multi-step reasoning.
Ability to work in ambiguous settings and bring clarity and structure to complex situations.
Experience building LLM evaluation pipelines using golden datasets, LLM-as-judge, and regression suites, and using them to gate releases.
Strong Python skills and experience with APIs, cloud deployment, observability, and debugging distributed systems.
Fluent written and spoken English, with the ability to write clear design documents and decision records.

Nice To Haves

Experience with Arabic and its dialects, especially Gulf Arabic, in LLM or speech systems.
Experience with ASR, TTS, and real-time voice agents, including latency optimization.
Hands-on use of LLM tracing and online evaluation tools such as Langfuse, Arize, or similar.
Experience fine-tuning or distilling open-weight models and serving them cost-effectively.

Responsibilities

Own and be accountable for the AI architecture and harness, including model selection, prompting patterns, retrieval design, guardrails, and the voice pipeline.
Make and document technical decisions with clear rationale.
Define evaluation standards and move key work from experimentation to production.
Introduce testing frameworks for agentic development.
Ensure production monitoring and online evaluations detect quality drops, hallucinations, and latency spikes.
Lead the AI team in designing, developing, and delivering the agentic platform.
Mentor AI engineers, raise the technical bar through pairing and feedback, and contribute to hiring.
Write production code and lead debugging of critical production incidents involving AI behavior.
Manage prototypes and experiments to validate new models, agent patterns, and voice features before the team commits to them.
Work with Product to turn customer use cases into technical requirements.
Work with Engineering on shared platform work, including tool calling, workflows, deployment, and scaling.
Take ownership of outcomes end to end, including gaps between teams, and follow through.
Explain technical trade-offs between quality, speed, and cost, and question existing approaches constructively.
Support teammates, share credit, and prioritize team goals while championing innovation.