Staff Platform Architect, Data & AI (Remote)

Experian is a global data and technology company, powering opportunities for people and businesses around the world. We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more. Experian invests in people and new advanced technologies to unlock the power of data. We have an amazing team of 25,200 people in 32 countries. About the Role: We are looking for a Staff Platform Architect to join our Data & AI Platform Architecture team. We are a small, high-use group that shapes technology strategy across analytics products, AI/ML enablement, and data infrastructure at enterprise scale. This is a role for someone with deep fundamentals in data, analytics, and MLOps platforms. You should also know how to evolve them to serve both humans and AI agents, internal and external, with equal thoughtfulness. You will extend and evolve a set of existing platforms including our MLOps infrastructure, batch platform, analytics stack, and managed analytics offerings, while leading greenfield design of our AI-ready data foundation. You will report to the Sr. Director of Platform Engineering. This is an individual contributor position. What you'll do here - Evolve our existing batch, analytics and MLOps platforms improving reliability, cost, and operational efficiency. - Develop the infrastructure for our semantic and ontology layers. (including authoring and governance tooling, lifecycle management, and catalog integration) - Design the usage infrastructure, including APIs, libraries, and services.

This infrastructure should make these layers usable by any downstream consumer, such as internal users, client-facing products, and AI agents. The infrastructure should also include permission-aware semantic discovery. - Guide technology adoption across engineering teams by making the right architectural choices well-reasoned and easy to follow. - Lead focused prototyping and R&D efforts with analytics product and engineering teams to validate new AI and analytics capabilities before broader platform investment. - Mentor engineers across the organization in your areas of expertise, with a focus on first-principles thinking, system design, and product awareness. - 10+ years of software engineering experience, with a deep focus on data platforms, analytics infrastructure, and AI/ML systems at enterprise scale. - Bachelor's Degree or higher in science, technology, engineering or related field - Experience building or operating MLOps platforms from data access and feature engineering through model deployment and monitoring. - Experience with data modeling, schema design, and data quality as platform engineering concerns. - Hands-on experience with AI agent-based architectures for governed data access, semantic discovery, and retrieval over enterprise data assets. - Experience designing federated catalog architectures that deliver unified data access across platforms and data silos. - Experience with security, compliance and governance considerations for AI/ML workloads, including data residency and access control. - Experience with distributed computing, cloud-native infrastructure, and the cost and operational dynamics of running large-scale data workloads on public clouds such as AWS. - Comfort with infrastructure as code and operating production workloads - Demonstrated experience managing and evolving shared platform architectures over time. - Experience driving architectural agreement across teams through technical influence, reference architectures, and hands-on collaboration, rather than through formal authority alone. - Background in credit risk, financial services, or other regulated data domains where governance and compliance constraints shape platform design.