Job responsibilities
Set the technical direction and architecture for the ALMonD web platform across the front end, service layer, and integration with the simulation engine, with a focus on scalability, performance, and long-term maintainability.
Build and maintain full-stack features end to end, from React/TypeScript user interfaces through Python services and APIs, with Java services where needed, deployed on AWS.
Design intuitive workflows for configuring, running, and comparing interest rate simulations and scenarios, partnering with Treasury, CIO, and risk stakeholders to translate quantitative requirements into usable product features.
Develop high-performance data visualizations, grids, and dashboards that render large simulation result sets (e.g., NII and EVE sensitivities, scenario comparisons, time series) with speed, clarity, and accuracy.
Lead the team's adoption of AI-assisted engineering practices using enterprise-authorized tools (e.g., for coding, refactoring, test generation, code review, and troubleshooting), and set clear standards for validating AI outputs for correctness, performance, and security.
Identify and prototype opportunities to bring AI into the product and engineering workflow, such as natural language scenario setup, AI-generated explanations of simulation results, and automation of repetitive development and operational tasks.
Set and enforce engineering standards for accessibility, performance, security-by-design, and automated testing (unit, integration, and end-to-end).
Write secure, high-quality production code and comprehensive tests, and contribute actively to code reviews as author and reviewer with a focus on maintainability, clarity, and consistency.
Build observability into front-end and back-end services (logs, metrics, tracing, real-user monitoring), troubleshoot production issues, take part in incident response as needed, and automate fixes for recurring problems.
Act as a technical mentor and multiplier for the team through design reviews, pairing, and hands-on guidance, influencing engineering direction without formal people management.
Collaborate with Product, Treasury, CIO, Risk, and Quantitative teams to refine requirements, break down work, and deliver iteratively across Agile increments.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience.
Advanced Python proficiency, including building production services and APIs, type hints, packaging, testing, and performance tuning.
Working experience with Java services, with the ability to contribute to and review Java code alongside Python.
Advanced proficiency in React and TypeScript/JavaScript, including modern state management, component-based architecture, and front-end performance optimization.
Strong hands-on system design and full-stack application development skills, with demonstrated production ownership (delivery, stability, incident response, and continuous improvement).
Experience building data-heavy user interfaces, including large interactive grids, charts, and dashboards.
Proven experience building authenticated API integrations (REST and streaming/WebSocket), including SSO, pagination, retry/backoff, and robust error handling.
Hands-on experience building and deploying applications on AWS, along with modern CI/CD practices, including automated testing, linting, type checking, and a disciplined git/PR workflow.
Demonstrated experience using enterprise-authorized AI-assisted development tools in day-to-day engineering, with the ability to set team expectations for using them responsibly and validating their outputs.
Strong understanding of web security, accessibility (WCAG), and front-end performance best practices.
Proven track record of technical leadership, including driving architecture decisions, mentoring engineers, and influencing across teams without direct authority.
Preferred qualifications, capabilities, and skills
Preferred financial services, especially Treasury, asset liability management (ALM), interest rate risk, fixed income, or balance sheet analytics.
Exposure in building user interfaces for simulation, modeling, or analytics platforms with long-running computations.
Hands-on experience building generative AI/LLM features into products, such as retrieval-augmented generation (RAG), tool/function calling, or agent workflows.
Familiarity with emerging AI standards and patterns such as MCP (Model Context Protocol) and agent skills.
Familiarity with micro-frontend architectures, design systems, or shared UI component libraries.
Exposure to distributed computing or cloud data platforms on AWS.
Familiarity in building in regulated environments with strong audit, access control, and change management requirements.
