Job Description
Overview The Lead Backend Engineer on the AI Platform team plays a critical role in building, evolving, and leading the backend systems and engineering practices that power internal products, customer-facing capabilities, AI-enabled workflows, and operational decision-making across the organization. This role combines hands-on backend engineering depth with technical leadership, delivery ownership, and mentorship for engineers working on business-critical systems. This role contributes to the development and evolution of core backend capabilities, including service-oriented architecture, APIs, workflow orchestration, event-driven integrations, identity and permissions, and operational tooling. In addition, the Lead Backend Engineer will drive buildouts of AI application infrastructure, LLM-powered product capabilities, agentic workflows, retrieval-augmented systems, evaluation pipelines, and shared platform patterns. Success requires strong technical judgment, sound systems thinking, and the ability to guide a team toward simple, scalable, and maintainable solutions in a cloud-based, regulated, high-stakes environment. The Lead Backend Engineer is expected to operate effectively in a modern engineering environment, using automation, observability, CI/CD, testing, infrastructure-as-code, and AI-assisted development practices to deploy, manage, and improve backend systems. In parallel, this individual will help set technical direction, break down ambiguous problems, mentor engineers, raise the quality bar through design and code review, and partner closely with Product, AI, Data, Operations, and business stakeholders to build both with AI and on top of AI responsibly, securely, and pragmatically. This is a fully remote position that offers a competitive salary range of $220,000 to $300,000, plus an annual bonus. You'll also receive our excellent benefits package, which includes medical coverage starting on day one and a company-matched 401(k). Compensation may vary based on experience, location, and other job-related factors. Responsibilities Scale High-Performance Distributed Systems Design, build, and maintain production backend services for a wide variety of internal and external use cases, including product workflows, operational tools, integrations, APIs, and AI-enabled applications Develop well-structured APIs, domain models, service interfaces, and business logic that are easy to understand, test, operate, and extend Build scalable backend workflows that support complex business processes across loans, documents, accounts, users, permissions, vendors, and operational decision making Remain hands-on in critical areas of the codebase, especially where technical direction, architectural leverage, incident resolution, or execution speed requires senior engineering judgment Technical Direction & Architecture Lead the design of service architectures that support transactional, operational, analytical, and AI-driven workloads across production environments Define practical patterns for service boundaries, idempotency, consistency, retries, failure handling, schema evolution, versioning, backward compatibility, and operational ownership Guide technical design reviews, architecture discussions, and implementation plans to ensure systems are simple, secure, reliable, observable, and maintainable Make sound technical tradeoffs that balance speed, simplicity, reliability, security, cost, and long-term platform leverage AI Product & Platform Development Lead the design and implementation of LLM-powered backend capabilities, including retrieval, tool use, workflow orchestration, structured outputs, human-in-the-loop review, evaluation, guardrails, and production monitoring Establish patterns for integrating AI systems with core services, data stores, document workflows, permissions, audit trails, operational decisioning, and user-facing product experiences Use AI-assisted development tools thoughtfully to accelerate software delivery while maintaining strong standards for code quality, testing, security, maintainability, and human ownership of technical decisions Partner with AI, Data, Product, and Operations teams to translate model capabilities, business workflows, and user feedback into reliable product experiences that improve over time Team Leadership & Delivery Lead, mentor, and develop backend engineers through technical guidance, design feedback, code review, pairing, coaching, and clear expectations for engineering quality Translate ambiguous business, product, and operational needs into clear technical plans, milestones, sequencing, risks, and execution paths for the team Coordinate delive