Senior Staff Software Engineer (AI/ML) at Experian

Job description   The AI Enablement team is building a unified platform that empowers engineering teams to rapidly and safely ship AI‑powered features. As a Tech Lead / Senior Full-Stack Engineer, you will provide architectural leadership across backend (MCP infrastructure, services) and frontend (widget host app, UI contribution surface). You will define and evolve the contribution models that enable teams to plug functionality into the platform consistently. This role requires strong technical breadth across .NET and Node/React, a strict test-first mindset, the ability to define and defend architectural boundaries, and a platform-team mentality where success is measured by what other teams deliver using your foundation. Key Responsibilities Architecture & Full-Stack Leadership • Lead architectural decisions across backend APIs, MCP services, frontend contributions, and cross-cutting platform components. • Define and evolve contribution models and extension frameworks across UI and backend surfaces. • Ensure clear boundaries between platform, product, and extension layers—and be able to defend them technically and organizationally. • Guide teams in system design, scalability, observability, and maintainability.Hands-On Development • Contribute production-grade code across both .NET backend services and Node/React/TypeScript frontend components. • Build APIs, platform capabilities, UI frameworks, and developer tooling aligned with platform simplicity and extensibility. • Maintain high performance, security, reliability, and DX-first design.Test-First Engineering • Champion a strict test-first approach: TDD, contract testing, integration tests, automated quality gates. • Build testing standards that apply consistently across backend and frontend surfaces.LLM Platform Integration • Apply a pragmatic understanding of LLM fundamentals—tokens, context windows, tool descriptions—to shape platform boundaries. • Collaborate with the AI platform to design tool invocation flows, prompt interfaces, and safe execution patterns. • Deep theoretical AI knowledge is not required—but rapid, confident learning and hands-on integration is expected.Platform Team Mindset • Operate with a service mindset: your success is defined by what other teams build successfully on your platform. • Create documentation, reference implementations, architectural guardrails, SDK patterns, and contribution guidelines. • Drive alignment across engineering groups to ensure the platform becomes the default mechanism for AI integration.