• Monitor daily dashboards and alerts to ensure AI workflows complete successfully • Identify, restart, and troubleshoot failed or stuck processes in real time • Perform light debugging and escalate complex issues to the Product or Engineering teams • Maintain consistency in uptime, reliability, and data flow across systems • Track and analyze key performance metrics to identify recurring errors or inefficiencies • Collaborate with Product and Engineering to refine and improve AI model behavior • Annotate or validate data outputs to ensure AI accuracy and consistency • Contribute insights that drive measurable improvement in system reliability • Use tools like **Extend**, **Basepilot**, and internal platforms to manage AI operations • Test and evaluate new frameworks and automation tools that could improve performance • Document repeatable troubleshooting playbooks and standardize operational workflows • Maintain logs of incidents, resolutions, and root causes for visibility and learning • Run light **SQL queries** and use **Postman or API clients** to verify data flows • Create or enhance dashboards and reports that surface system health and performance

• Drive measurable improvements in key customer and business metrics (e.g. conversion, activation, retention, task success) through design-led experimentation. • Run design experiments, partnering with data, product and engineering to define hypotheses, success metrics, and evaluation frameworks. • Design and iterate on customer-facing experiences across Kraken’s trading, onboarding, and account surfaces based on experiment results and user insight. • Apply AI selectively and responsibly to improve personalization, decision support, and usability, with a clear focus on outcomes and user trust. • Own design quality and experiment performance, taking accountability for learnings, trade-offs, and results. Not just outputs. • Influence product strategy by translating experiment insights into clear recommendations and next steps. • Balance speed and rigor, enabling rapid experimentation while maintaining high standards of UX, accessibility, and brand consistency. • Contribute actively to design reviews and experiment readouts, sharing insights that raise the effectiveness of design and experimentation across teams. • Continuously identify new opportunities where experimentation and AI can unlock customer or business value.

• Responsibility for analyzing end-to-end damage processes and adjacent workflows • Identifying, evaluating, and prioritizing AI and automation opportunities with strategic impact • Leading the design and conception of AI agents and agent-based workflows, including governance and human-in-the-loop • Developing MVPs and transitioning them into production • Technical integration into existing system landscapes (APIs, RPA, OCR, data sources) • KPI-based monitoring and coordination of internal teams and external partners at the corporate level

• Lead end-to-end recruiting from job scoping to hiring strategies • Implement tooling for efficient onboarding and exceptional experiences • Collaborate on learning opportunities that increase engagement and performance • Support team leaders to align on goals for delivering outcomes • Measure effectiveness of people strategies using data analysis • Enhance operational efficiency using AI tools, LLMs, and automation