AI Tech Lead
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 2d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 13d ago
The date the source published, not the day we noticed it (2026-10-20). Last seen at its source 4h ago.
We have tracked this listing since 20 Oct 2026 (12 days). The employer's own board has carried it every time we have read it, most recently 4 hours ago.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Saudi Arabia
The description states no restriction of its own. This is the source's own tag.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
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Workable employer's own board first seen 12d ago · last seen 4h ago
The listing
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.