• Embed in each function before building anything — learn how work actually moves, surface the workflows worth automating, and build them using the platform stack the team is assembling. Production adoption is the metric, not a polished demo. • Configure and deploy the End User Workbench for each function you support — tailored to how that team actually works, not a generic starting point. • Scope, build, and ship agentic workflows in production across business functions like legal review, financial operations, marketing, HR, and revenue. These are not prototypes. • Evaluate AI-native vendor solutions alongside the functions you support — separating genuine capability from hype and making honest build-versus-buy recommendations. When something real exists, recommend it. When it doesn't, build it. • Work alongside people on real problems until they can scope, build, and own their own agent workflows. The measure is when teams you support are shipping without you. • Support your partners through agent ownership — what it means to maintain a workflow when you've never operated software before. You'll work through those questions in practice, not in theory. • Return field signal to the platform. What you learn forward-deployed shapes how the End User Workbench evolves — and in some cases you will build the solution directly into it. • Partner with Legal, Privacy, and Security to keep data boundaries built into every workflow from the start — not appended at the end. • Document what works — configuration templates, workflow patterns, and deployment approaches that make the next engagement faster and safer.

• Oversee the performance and reliability of 1NCE’s automated first-line customer support platform. • Monitor the daily performance of the automated first-line AI support system. • Review operational dashboards, workflow execution logs, and ticket flows to ensure system stability and performance. • Identify workflow interruptions, failed automation steps, or abnormal behavior within the AI support system. • Ensure SLA adherence for automated support processes. • Manage operational incidents affecting automated support workflows. • Identify and resolve basic operational issues within AI-driven workflows. • Escalate technical problems to engineering or platform teams when required. • Ensure rapid recovery of automated support processes during incidents. • Manage complex or exceptional customer cases that cannot be resolved by automated workflows. • Monitor AI support response quality and resolution performance. • Identify improvement opportunities in automation workflows and operational processes. • Coordinate with internal teams such as Customer Support, Product, Platform Engineering, and AI operations teams.

• Design and solve challenging Physics problems to probe the limitations of large language models. • Create clear, high-quality, step-by-step solutions with well-articulated reasoning. • Collaborate with LLM researchers to align problems with evaluation goals, especially in areas where models struggle (e.g., abstraction, multi-step reasoning, symbolic manipulation). • Help define new evaluation benchmarks based on Physics curricula spanning early undergraduate to PhD-level topics.

• Create and implement Python scripts to automate tasks and integrate with third-party systems via APIs. • Work with RESTful APIs, performing create, read, update, and delete (CRUD) operations on services. • Develop automated tests to ensure the integrity and functionality of API integrations. • Maintain clear and detailed documentation of the automation and integration processes developed. • Work closely with other teams to understand requirements and implement effective solutions.