Lead evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
Lead communities of practice across Software Engineering to drive awareness and adoption of new and leading-edge technologies
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
Foster a team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and advanced applied experience
Hands-on practical experience delivering system design, application development, testing, and operational stability
Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
In-depth knowledge of the financial services industry and its technology systems
Practical cloud-native experience
Experience with Java, Spring Boot, Spring MVC, and Spring Data in a production environment
Demonstrated ability to design and deliver microservices with high reliability, clear APIs, and strong test coverage
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices
Preferred qualifications, capabilities, and skills
Strong hands-on experience with Go, building and maintaining production-ready services and tooling
Experience with Starlark for build and automation workflows, including writing and maintaining Starlark scripts and rules
Working knowledge of Java and Groovy for integrations and supporting components in mixed-language environments
Comfortable with scripting (Shell/Python as needed), debugging, and collaborating across code reviews, CI/CD, and release workflows
Proficiency in API design principles including REST best practices, versioning, and backward compatibility
Experience with relational and/or NoSQL databases, including indexing, transactions, and migrations
Familiarity with observability tooling such as distributed tracing, log aggregation, and metrics platforms
Experience with containerization and orchestration technologies such as Docker and Kubernetes
Experience writing unit, integration, and contract tests using testing frameworks and libraries
