• You will build a best-in-class web experience for sophisticated retail traders on a new, AI-native active trading platform • You will ship core active trading workflows: order entry, positions/holdings, blotter/activity, watchlists, alerts, and high-information-density views • You will build AI-native UI patterns that earn trust: streaming answers, grounded citations/context, uncertainty signaling, pre-trade confirmations, and safe “undo/rollback” experiences where applicable • You will work closely with users and stakeholders to define features, iterate quickly, and drive projects to completion • You will collaborate deeply with backend engineers to design clean contracts for low-latency trading interactions and AI tool integration • You will develop a deep understanding of the markets/trading domain and how to translate it into intuitive, powerful UX

• You will develop a high-performance backend in Rust that supports streaming responses, low-latency tool execution, caching, and reliable orchestration of model + tools • You will build the core AI platform that powers a copilot inside a new, AI-native trading experience • You will design robust APIs and runtime components for AI capabilities: authentication/authorization, rate limits, auditing, tracing, retries, and fallbacks • You will build safe action infrastructure for trading workflows: deterministic order previews, confirmation flows, idempotent tool calls, and comprehensive audit logs • You will partner with product and frontend to enable great AI-native UX patterns (streaming, citations/grounding, "why" explanations, reversible actions) • You will develop a deep understanding of the business domain and help translate it into secure, resilient platform primitives

• Work closely with research engineers to develop productionized ML workflows • Develop and iterate on platform infrastructure for training, validating, and deploying neural networks • Deploy cloud infrastructure and build distributed data-processing pipelines • Develop on an in-house multi-tenant data lake ecosystem supporting the entire lifecycle of machine learning operations • Build internal APIs to serve data, trigger workflows, enforce business logic, etc. • Build CI/CD process and other internal tools to enhance developer productivity

• Configure and maintain AWS services including VPCs, EC2 instances, queues, DocumentDB, load balancers, security groups, Kubernetes clusters, and container repositories, along with network security controls (firewalls, WAF, and network ACLs). • Build and manage Jenkins jobs and CI/CD pipelines that produce deployment artifacts, including ZIP packages and Docker images; maintain Kubernetes deployment configurations (kubectl, YAML). • Write Linux utility scripts to support cluster management, automate deployments and operational tasks, and troubleshoot infrastructure issues across distributed environments. • Develop and maintain applications in C#, T-SQL, Vue, HTML, and CSS; troubleshoot issues across the full stack including UI, API, database, messaging, and deployment layers. • Build and maintain core application services on ServiceStack and the LLBLGen ORM data-access layer; customize and support platform-integrated applications such as Umbraco and Identity Server. • Maintain and troubleshoot integrations between internal systems and external platforms; manage Git-based source control workflows (GitHub/GitLab) including branching, code reviews, and coordinated deployments. • Work with SQL Server and T-SQL for querying, troubleshooting, and supporting application data. • Support MongoDB/DocumentDB document storage and reason about relational and document database usage across application services. • Design and support event-based, message-driven architecture using MassTransit over message brokers such as Amazon SQS, RabbitMQ, and Apache MQ across distributed application services. • Apply AI-assisted development tools (e.g., GitHub Copilot, Devin) to coding, debugging, documentation, and PR workflows to measurably improve team productivity. • Review, validate, and govern AI-generated code and infrastructure-as-code; evaluate emerging AI productivity tools and recommend adoption across engineering teams. • Adapt deployment processes and engineering practices as platform requirements and tooling evolve. • Mentor and guide engineers across the stack, raising the bar on code quality, architecture, and operational practices. • Lead technical decisions and provide hands-on guidance on complex, cross-cutting problems from development through production troubleshooting. • Partner with engineering leadership to set technical direction, plan coordinated releases, and onboard and grow team members.