Lead Software Engineer - Public Cloud, Foundational Infra Platforms

As a Lead Software Engineer in Infrastructure Platforms Cloud Foundation Services, you help shape the base cloud layer used by thousands of engineers. You will contribute to multi-cloud foundations spanning AWS, Azure, and Google Cloud, influencing platform design and operation. You’ll solve scale problems, build secure, reliable software, and mentor peers while advancing enterprise-wide AI-assisted engineering practices. This role offers impact across provisioning platforms and cloud-native infrastructure in a fast, collaborative environment.

• Develop secure, high-quality production code for cloud platform services and tooling, including code review and debugging
• Own significant technical design decisions and contribute to product design and platform architecture
• Build deep knowledge of the platform and disseminate it to prevent critical capabilities from being bottlenecked to a single engineer
• Mentor other engineers and raise engineering practices within the team
• Contribute to the engineering community through firmwide frameworks, tools, and SDLC practices
• Influence peers and project decisions to adopt leading-edge technologies
• Drive adoption of AI-assisted engineering practices to improve quality, speed, and reliability with standardized validation and reuse of patterns
• Apply SDLC tooling and AI-enabled development and automation capabilities to increase automation value
• Foster a diverse, inclusive, and respectful team culture

• Formal training or certification on software engineering concepts and advanced applied experience
• Advanced proficiency in one or more programming languages (Java, Python, or Go)
• Hands-on experience with at least one major public cloud (AWS, Azure, or Google Cloud)
• Hands-on experience with Terraform for infrastructure as code, from a software engineering perspective
• Experience designing, developing, and maintaining production software systems used by other engineering teams
• Experience owning technical design decisions and mentoring other engineers
• Knowledge of cloud-native architecture, distributed systems, and microservices design patterns (scalability, reliability, fault tolerance)
• Proficiency across the SDLC (design, development, testing, deployment)
• Hands-on experience with enterprise-authorized AI-assisted software development tools with ability to evaluate outputs for correctness, performance, and security
• Understanding of responsible AI use in engineering, including data sensitivity and secure handling

• mentoring
• collaboration
• problem-solving
• Java
• Python
• Go