Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
Required qualifications, capabilities and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
Demonstrated hands-on experience designing and delivering scalable, secure, resilient distributed systems in a production environment
Proficiency in Java and Spring Boot, with working knowledge of SQL and modern user interface frameworks (for example, React)
Strong understanding of modern architecture patterns, including microservices, event-driven design, and application programming interface-first approaches
Experience building and operating solutions on public cloud platforms (for example, Amazon Web Services), including containerization and orchestration (Docker and Kubernetes)
Experience implementing continuous integration and continuous delivery pipelines and Infrastructure as Code practices to improve release safety and speed
Practical experience with application programming interface design and integration patterns (for example, REST and GraphQL), including security and performance considerations
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
Preferred qualifications, capabilities and skills
Advanced expertise in object-oriented design, system design, and performance optimization for large-scale services
Experience with modern data and search technologies (for example, Elasticsearch) and databases (for example, Oracle or MongoDB)
Experience building generative AI solutions, including retrieval-augmented generation architecture, orchestration frameworks (for example, LangChain or LlamaIndex), and evaluation practices
Experience creating reusable agent skills, libraries, or patterns that accelerate delivery across engineering teams
Experience working across hybrid technology ecosystems using cloud services, Databricks, and Kubernetes-based platforms
