• Design, implement, and ship products that integrate AI capabilities for users • Build responsive front-ends, scalable back-end services, and database/pipeline plumbing • Collaborate with AI engineers to integrate models, agents, and retrieval systems into applications • Own product testing, observability, and feedback loops • Work closely with engineers, designers, and strategists throughout project life-cycle

• Architect and build AI-powered automation that runs underneath our go-to-market engine. • Design and ship the agents, workflows, and integrations for customer lifecycle management. • Build intelligent orchestration connecting sales, marketing, partnerships, and customer success. • Engineer automation behind the customer lifecycle, ensuring measurable progression. • Translate behavior and health signals into automated nudges for commercial teams to act on. • Build operational infrastructure for partner activation, onboarding workflows, and performance dashboards. • Maintain integrations across business systems ensuring clean data flow and governance. • Proactively hunt for automation opportunities across customer and partner lifecycle.

• Build full-stack applications across our credit, private credit, and structured products platforms — backend services, APIs, data pipelines, and modern web front ends used by various business teams across the firm • Develop quantitative models and analytics for fixed-income and structured product valuation, cash flow projections, scenario analysis, and portfolio risk decomposition • Integrate third-party systems including Geneva (portfolio accounting), market data vendors, CRM platforms, and administrative platforms, design clean, well-tested adapters and reconciliation logic • Participate in the Migration of legacy .NET/C# applications and SSRS reports to modern, scalable architectures (TypeScript/React front ends, Python or .NET services, cloud-deployed) with responsive UX across desktop and mobile • Own data quality end-to-end - ingestion, normalization, validation, and lineage - for firmwide positions, partnering with the data management team on governance and controls • Build reporting and BI spanning Tableau dashboards, internal web tooling, investor reporting, and ad-hoc requests for portfolio composition and DDQ responses • Translate business needs into engineering — gather requirements directly from PMs, analysts, risk, IR, and operations; document functional and technical specs; write clear UAT plans and lead testing • Ship like an engineer - write tests, use source control (GIT/TFS), open clean PRs, manage tickets in DevOps, deploy through CI/CD, and monitor what you ship in production • Use AI coding assistants well - accelerate delivery, reduce boilerplate, and improve code quality, while applying the verification, security, and review standards described later in this document

• Build modern web applications - Design, develop, and maintain full-stack applications using React, JavaScript/TypeScript, Node.js, and AWS technologies. • Develop backend services and APIs - Build scalable services and integrations that connect applications with internal platforms, data services, and third-party systems. • Create great user experiences - Develop responsive interfaces and workflow tools that simplify complex business processes and data visualization. • Collaborate across teams - Partner with Product, Data Engineering, Analytics, and Data Science to translate business requirements into thoughtful technical solutions. • Write high-quality software - Develop clean, maintainable, well-tested code while participating in code reviews and following engineering best practices. • Contribute to technical design - Participate in architecture and design discussions, helping shape solutions that balance scalability, maintainability, and speed of delivery. • Continuously improve - Identify opportunities to improve application performance, deployment processes, developer experience, and internal workflows.