Role Description We’re looking for a Senior Software Engineer, Fintech who will help advance the quality, efficiency, and scalability of our engineering team by: - Building product features - Improving developer workflows - Strengthening security controls - Designing shared technical foundations - Helping engineers move faster with better tools and practices Qualifications - 7+ years of professional software engineering experience - 3+ years of experience with Node.js, TypeScript, and MongoDB, or equivalent experience with comparable technologies - Experience contributing directly to compliance activities in a SOC II, ISO-27001, or similar environment - Understanding of backend architecture, API design, distributed systems, and production-grade software development - Experience with AI-assisted development tools such as Cursor, CodeRabbit, Claude, GitHub Copilot, or similar Requirements - Entrepreneurial self-starter with a focus on delivering measurable results - Strong organizational and efficiency skills - Master communicator with the ability to translate complex technical concepts - Understanding of regulatory, security, privacy, and operational expectations in banking and fintech - Tech-forward with analytical thinking skills - Commitment to being a team player Benefits - Comprehensive, people-first benefits package - 100% employer-paid health coverage for employees - 401(k) with 6% employer match and no vesting period - Generous paid time off: 2–4 weeks of vacation based on officer level - Company-paid life insurance - Employee assistance program (EAP) with free counseling, legal, and financial services

• Own agents end to end. • Take a feature from prototype to production: orchestrator and sub-agent design, the tools the agent calls, system prompts, memory, and the response contract the frontend renders from. You write the code that ships. • Own the agent runtime. Design the production runtime to stay fast on the member-facing path and easy to debug when something breaks. • Make the tools trustworthy. Build the tool layer agents depend on, like search grounded in our real catalog, retrieval and reranking, and cart and account actions. • Own safety. Build the layered safety model: input and output guardrails, intent and clarification handling, refusals, and PII boundaries. • Make quality measurable. Push our evaluation work forward: structured checks plus LLM-as-judge, with a review queue for the cases the judges disagree on. • Instrument it. Make agents debuggable in production with per-session and per-turn timelines, tool and guardrail traces, and token and cost visibility. • Turn it into a platform. Take the patterns that work and make them reusable, so the next agent and the next engineer inherit the runtime conventions, the eval scaffolding, and the guardrail defaults instead of starting over. • Make the team better. Set technical direction across the agent codebases and infra, and keep design and code review sharp.

• Design and implement automated, reusable training pipelines to ensure consistent, scalable model delivery across the partner portfolio. • Build ETL pipelines with thoughtful feature engineering to guarantee clean, reliable inputs for pricing models. • Develop and deploy real-time ML pricing solutions to production, owning the full path from model to live environment. • Monitor production systems for latency, drift, and training-serving skew, optimising continuously to maintain model integrity. • Run champion-challenger tests on pricing and product construction levers to surface improvements and respond to shifting market conditions. • Partner with data scientists, engineers, and product stakeholders to translate business needs into well-scoped technical solutions.

• Design, develop, and deploy scalable GenAI applications using LLMs, RAG, AI agents, and workflow orchestration frameworks • Build production-grade AI systems integrating structured and unstructured enterprise data • Architect and optimize end-to-end AI pipelines • Develop AI-powered copilots, assistants, automation workflows, and autonomous agent systems • Design hybrid AI systems combining deterministic workflows with autonomous agent behavior • Build multi-agent orchestration workflows • Implement tracing, telemetry, observability, and monitoring • Develop automated evaluation pipelines and testing frameworks • Improve reliability through retrieval optimization and AI safety mechanisms • Optimize inference cost, latency, throughput, and scalability • Own AI systems from prototype to production • Collaborate with stakeholders, product managers, platform teams, and data engineers • Stay current with advances in LLMs, agentic AI, multimodal systems, and AI infrastructure