Staff Software Engineer, Data & AI Platform Architecture

Summary

Join Experian as a Staff Software Engineer and help shape the future of our enterprise-wide Data, Analytics, and AI/ML platform. This role focuses on building foundational capabilities—architecture, infrastructure, and reusable services—that power data-driven and AI-enabled products. You will architect and build core platform components, define solution architectures, lead build vs. buy evaluations, stay current with modern data and ML architectures, partner with various teams, guide platform integration, drive internal adoption, and mentor engineers. The position requires 8+ years of software engineering experience with a deep understanding of building platforms for data, analytics, or ML/AI workloads. Experian offers a great compensation package, core benefits, a flexible work environment, and flexible time off.

Requirements

• 8+ years of software engineering experience, with a deep exposure to building platforms for data, analytics, or ML/AI workloads
• Background in distributed systems, cloud-native architecture, and data-intensive platforms
• Proficiency in Python, Java, or Scala
• Experience with big data processing frameworks (e.g., Spark, Flink) and modern data architectures (e.g., Lakehouse, Delta Lake, Apache Iceberg)
• Experience with cloud platforms (AWS preferred), including Terraform, Helm, or other Infrastructure-as-Code tools
• Solid knowledge of Docker, Kubernetes, and building production-grade CI/CD pipelines
• Track record of architectural leadership, influencing technology adoption and driving platform reuse across teams

Responsibilities

• Architect and build core platform components that support the entire data, analytics, and AI/ML lifecycle — including data processing, feature engineering, model training/serving, observability, and governance
• Define solution architectures for internal platform capabilities and reference implementations for common AI/analytics use cases
• Lead the build vs. buy evaluation for components such as MLOps frameworks, vector stores, orchestration layers, and AI development tools
• Stay current with modern data and ML architectures, including lakehouses, LLM orchestration patterns, and multi-tenant model serving
• Partner with engineering, data science, and product teams to enable enterprise-scale adoption of shared platform services
• Guide platform integration with public cloud services (AWS preferred), CI/CD pipelines, and observability stacks
• Drive internal adoption by influencing engineering teams across global and regional product lines
• Mentor engineers on platform best practices, architecture decisions, and scalability patterns

Benefits

• Great compensation package and bonus plan
• Core benefits, including medical, dental, vision, and matching 401K
• Flexible work environment, ability to work remotely, hybrid, or in-office
• Flexible time off, including volunteer time off, vacation, sick, and 12-paid holidays