Lead AI Engineer - Innovation unit - Skai

0B in annual ad spend through partnerships with Google, Meta, Amazon, Microsoft, and others.

Founded in 2006, Skai is a global company with around 650 employees. We operate in a hybrid work model that combines remote flexibility with in-office collaboration.

What will you do?

We are the Agents Studio group at Skai, operating as a high-speed development group within a global powerhouse. Following the initial launch of our flagship Agents Studio platform MVP, we are now scaling through our live Beta phase.

In this role, you will serve as a GenAI-native technical lead, driving the rapid execution of our product strategy. You will connect live market feedback and customer needs directly to engineering delivery. Leading a dedicated pod of engineers, you will leverage advanced GenAI toolchains and translate real-world requirements into scalable, production-grade agentic solutions.

Responsibilities:

• Drive Live Business Impact & Beta Launch: Rapidly engineer GenAI-assisted solutions, prototypes, and platform adjustments that unlock customer value and support Beta adoption.
• Command the Rapid Execution Pod: Lead a dynamic pod of 2-3 engineers drawn from core teams. Establish an AI-first development culture and agentic workflows to maximize team velocity and focus on high-impact business needs.
• Frontline & Client Alignment: Partner closely with Forward-Deployed Engineers (FDEs),Product teams, and clients to translate user feedback and market friction into solid architectural solutions.
• Dynamic Backlog & Roadmap Generation: Convert market signals and customer feedback into an actionable engineering backlog, taking end-to-end ownership from concept to production.
• Navigate the MVP-to-PMF Gap: Make pragmatic technical decisions daily, balancing fast GenAI prototyping with long-term, scalable core platform architecture.
• Agentic Workflows & AI Velocity: Build, scale, and maintain systems using GenAI coding toolchains and LLM frameworks, improving engineering output and setting standards for human-AI collaboration.
• Context Encoding & Technical Guardrails: Encode domain and organizational knowledge into scalable context layers, prompt pipelines, and fallback mechanisms to ensure system reliability and enterprise safety.
• Mentorship & GenAI Culture: Drive AI-native development practices across engineering teams, mentoring on practical AI adoption and fast-paced delivery.