Lead software engineer (Full stack - Java) at Experian

Job description   Role Overview Join Experian Automotive and help power one of the industry’s most trusted vehicle data platforms. You’ll design and build high-scale, cloud-native systems that process millions of vehicle history and transaction requests daily, supporting major partners across dealerships, lenders, marketplaces, and enterprise platforms (e.g., Carvana, Cox Automotive, Cars.com, and others). This role blends modern distributed systems engineering with AI-assisted development and emerging agentic workflows, giving you the opportunity to shape both core platform architecture and the next generation of intelligent engineering practices. What You’ll Do ·       Own the design and delivery of high-performance, low-latency APIs and microservices powering vehicle history (VHR), VIN intelligence, and transaction systems. ·       Build and scale Java/Spring Boot services in a cloud-native AWS environment, handling high-throughput, real-time workloads. ·       Architect and implement event-driven systems using Kafka/SQS to support asynchronous processing and data pipelines. ·       Lead development of complex features, platform enhancements, and proof-of-concepts that drive business impact. ·       Contribute to and influence system architecture, scalability, and reliability decisions across services. ·       Implement and improve CI/CD pipelines, automated testing, and observability (logging, tracing, performance tuning). ·       Collaborate closely with product, data, and partner teams to deliver mission-critical integrations and APIs. ·       Participate in full SDLC ownership: design → build → deploy → operate (“you build it, you own it”). ·       Mentor engineers and contribute to a strong engineering culture through code reviews and technical leadership.   AI-Assisted Engineering (Required) ·       Leverage tools like GitHub Copilot, Codex, Cursor, and Claude Code to accelerate development, testing, and documentation. ·       Design and integrate AI-assisted workflows and agent-driven capabilities into engineering processes and/or production systems. ·       Build or support intelligent features (e.g., automation, recommendations, data enrichment, or decisioning) using AI models and APIs. ·       Develop tool-integrated agent workflows, including prompt design, multi-step orchestration, and API-based task execution (e.g., MCP or similar patterns).