• Design, build, and own end-to-end applications that support credit, private credit, structured products, and CLO businesses • Write production code across the stack, model financial cash flows and risk, integrate market data and pricing services, and partner directly with client solutions group, portfolio managers, risk, operations, and investor relations • Contribute to the modernization of the analytics and reporting platform - moving legacy applications to responsive, cloud-aware architecture • Replace ad-hoc spreadsheets with auditable services, and build the data and tooling layer that supports the firm’s various lines of business • Use AI coding assistants as a daily part of your workflow

• 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 • 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. • Approximate Time Allocation • 60% development — services, APIs, front-end, integrations • 20% reporting and dashboard development — Tableau, SQL, internal web tooling • 15% requirements gathering, analysis, and UAT • 5% production support and code review

• Design, develop, and deploy production-grade AI/ML and Generative AI solutions • Build and optimize RAG pipelines, embeddings, semantic search, and document intelligence workflows • Develop scalable APIs and full-stack applications supporting AI use cases • Collaborate closely with system architects across the full software lifecycle • Implement LLM evaluation frameworks, monitoring, and performance metrics • Apply AI safety guardrails including content moderation and hallucination reduction • Lead and mentor engineers through design, development, testing, and release cycles • Communicate technical tradeoffs and progress clearly to stakeholders

• Contribute to and influence architecture decisions across our stack, helping establish patterns the team builds on • Own complex product challenges end-to-end—from technical design through shipping to end users • Support and grow other engineers through code reviews, design feedback, and pairing • Partner with Product, Design, and Clinical teams to translate clinical workflows into scalable solutions • Identify and help drive improvements to technical debt, reliability, and developer experience • Actively use AI tools in your daily workflow and contribute to team-wide best practices