Staff Software Engineer, Data & AI Platform Architecture

Summary

Join Experian as a Staff Software Engineer and contribute to the development of our enterprise-wide Data, Analytics, and AI/ML platform. This role focuses on building foundational capabilities, not individual ML models, including architecture, infrastructure, and reusable services. You will report to the Senior Director of Platform Engineering and collaborate with various teams. Responsibilities include architecting and building core platform components, defining solution architectures, leading build vs. buy evaluations, staying current with modern data and ML architectures, partnering with other teams, guiding platform integration, driving internal adoption, and mentoring engineers. The ideal candidate possesses extensive software engineering experience, expertise in distributed systems and cloud-native architecture, and proficiency in various programming languages and technologies. Experian offers a competitive compensation package, comprehensive benefits, and a flexible work environment.

Requirements

• 5+ 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