The Staff Software Engineer at GEICO will focus on Java development and microservice-based architectures while leveraging Artificial Intelligence technologies. This role involves collaborating with Enterprise Architects and Product Managers to define technical roadmaps and integrating AI-powered features. The ideal candidate will mentor engineering teams and drive innovation within the software platforms. This position offers an opportunity to make a significant impact on the company's technology landscape.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
• 10+ years of professional experience building maintainable, scalable, and high-performance systems in Java.
• Expert in modern Java (preferably Java 21) and hands-on with the latest features and best practices.
• Deep knowledge of Spring Boot (preferably 3.4.x+), Spring Cloud, and related frameworks.
• Extensive experience in designing and deploying distributed systems and microservice-based architectures.
• Demonstrated hands-on experience with Generative AI applications and familiarity with AI concepts such as RAG and MCP.
• Experience using AI-assisted development tools (e.g., Claude, Cursor, Codex, or similar) to improve software engineering productivity.
• Strong understanding of RESTful API design, secure API development, and service-to-service communication patterns.
• Proficient with cloud technologies (AWS, Azure, or GCP) and containerization (Docker, Kubernetes).
• Solid grounding in CI/CD, automated testing, infrastructure-as-code, and observability.
• Excellent communication skills, a collaborative mindset, and experience mentoring engineers.

• Design, develop, and maintain scalable, high-performance, and modular microservices using Java (version 21+) and Spring Boot (3.4.x).
• Collaborate with Enterprise Architects and Product Managers to define technical roadmaps and architectural blueprints.
• Build and integrate AI-powered features, focusing on Generative AI solutions.
• Partner with ML and Data Science teams to utilize AI models and support cutting-edge use cases.
• Provide technical leadership and mentorship for engineering teams; establish best practices for code quality, security, testing, and DevOps.
• Review code, coach engineers, and foster an inclusive, learning-rich environment.
• Champion innovation, stay abreast of technology trends, and drive continuous improvement in engineering processes, observability, and automation.