AI Tech Lead
Lucidya
7–12 years of experience
Job description
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
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.