AI Engineer

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?

As an AI Engineer, your primary focus will be delivering working features while adopting a Builder Mindset to shape the future of software development at Skai. You will leverage cutting-edge GenAI tools (such as Claude Code and others) to accelerate development, streamline workflows, and bring high-quality, production-ready features to life.

In this role, you will work closely with PMs, designers, and fellow engineers across frontend, backend, and data domains. You will use AI to tackle complex debugging, conduct research, and eliminate repetitive tasks—continuously improving your productivity while delivering functional, impactful features within your pod.

Responsibilities:

• Feature Delivery: Own and deliver working features from end to end, contributing production-ready code with guidance and driving tangible product value.
• AI-Accelerated Development: Utilize GenAI tools (e.g., Claude Code) to accelerate feature development, improve personal productivity, and optimize daily workflows.
• Problem Solving & Debugging: Leverage AI for rapid debugging, technical research, and reducing repetitive manual work during feature delivery.
• Cross-Domain Collaboration: Build a solid understanding across product, frontend, backend, and data domains, collaborating effectively with PMs, designers, and engineers.
• Context & Documentation: Document technical decisions clearly and contribute to building shared team context and knowledge bases.
• Continuous Learning & Adaptability: Learn the tech stack quickly, adapt smoothly to new tools/workflows, and actively apply feedback to grow as a builder.
• Pod Reliability: Act as a dependable, collaborative contributor within your pod, helping drive team momentum and execution.