Staff Software Engineer, Data & AI Platform Architecture

Summary

Join Experian as a Staff Software Engineer and contribute to the development of their enterprise-wide Data, Analytics, and AI/ML platform. This role focuses on building foundational capabilities, such as architecture, infrastructure, and reusable services, to power data-driven and AI-enabled products across the organization. You will work with cross-functional teams to design and implement scalable, secure, and cost-efficient platform components that support GenAI, ML Ops, and advanced analytics use cases. You will also play a key role in driving the adoption of shared capabilities across business units and regions. This position offers the opportunity to architect and build core platform components, define solution architectures, lead build vs. buy evaluations, stay current with modern data and ML architectures, partner with engineering, data science, and product teams, guide platform integration with public cloud services, drive internal adoption, and mentor engineers.

Requirements

• 8+ years of software engineering experience, with deep exposure to building platforms for data, analytics, or ML/AI workloads
• Strong 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

Preferred Qualifications

• Experience building internal ML platforms, MLOps frameworks, or self-service data science environments
• Exposure to LLM-based applications and GenAI tooling (e.g., LangChain, vector databases, prompt orchestration)
• Understanding of security, compliance, and governance requirements for ML/AI workloads
• Familiarity with platform observability (logs, metrics, tracing) for distributed systems

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