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Open nowFirst seen 6 days ago

AI Engineer Team Lead - Innovation unit

Skai27 open roles

Where
Modi'in, Israel
Work mode
Hybrid
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Your applicationOpen nowAI Engineer Team Lead - Innovation unitSkai · Modi'in, Israel

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  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on Skai's own form.

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market. Skai postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.7%14 days
  5. 35.6%30 days
  6. 58.0%60 days
This job: first seen 6 days ago

Skai median: 1 days open

The posting

Who are we?

Skai (formerly Kenshoo) is a leading omnichannel marketing platform that leverages advanced AI and data-driven performance media to enable smarter decision-making, increased efficiency, and maximized ROI for businesses worldwide. Our platform helps manage approximately $10B 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.

Requirements

  • 5+ years of hands-on experience as a Senior or Lead Backend/Full-Stack Engineer, with a strong background in fast-paced startup or rapid-execution environments.
  • 2+ years of engineering leadership experience, including Tech Lead, Team Lead, or equivalent managerial roles.
  • Production AI Experience: Proven hands-on experience building, architecting, deploying, and operating AI Agents, GenAI frameworks, and LLM-based applications in production environments.
  • Startup DNA & Ownership: Demonstrated ability to thrive in dynamic environments, take extreme ownership, and balance strategic technical goals with practical execution.
  • Product-Minded Engineering: Strong business acumen and customer focus, with a track record of prioritizing engineering work based on product impact.
  • Architectural Expertise: Solid foundation in cloud infrastructure (AWS preferred), microservices, CI/CD pipelines, and building robust guardrails for AI systems.
  • Cross-Functional Leadership: Excellent communication skills, with experience leading engineering pods, aligning with Product, and interacting with client-facing teams.
  • Pragmatic Execution: Proven ability to deliver fast without sacrificing enterprise-grade code quality, security, and stability.
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