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Open nowPosted 15 days ago

AI Tech Lead

Lucidya

Where
Riyadh, Riyadh Province, Saudi Arabia
Work mode
Remote
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Your applicationOpen nowAI Tech LeadLucidya · Riyadh, Riyadh Province, Saudi Arabia

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Share of postings closed within
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  3. 13.3%7 days
  4. 21.3%14 days
  5. 35.7%30 days
  6. 59.1%60 days
This job: posted 15 days ago

The posting

About Lucidya

Lucidya builds an operating system for Agentic customer experience. Our flagship product line is an agentic platform (Agent Studio) where customers build and run AI agents that resolve real customer experience issues across text and voice channels, in Arabic and English.

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

Requirements

  • 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.
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