Develops secure and high-quality production code, and reviews and debugs code written by others
Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
Drives decisions that influence the product design, application functionality, and technical operations and processes
Serves as a function-wide subject matter expert in one or more areas of focus
Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
Proven hands-on experience delivering system design, application development, testing, and operational stability in production environments
Advanced proficiency in Java (enterprise backend development), with strong practical experience building services using Spring Boot (or equivalent Java frameworks)
Hands-on experience designing, building, and operating integrations using messaging and/or event streaming services (e.g., Kafka, AWS MSK, SQS/SNS, Kinesis), including reliable delivery patterns, error handling, and observability in production
Practical, hands-on cloud-native experience (the team’s target platform is AWS), including building/deploying/operating services in a cloud environment
Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls.
Ability to tackle design and functionality problems independently with little to no oversight
Preferred qualifications, capabilities, and skills
Reactive programming experience (e.g., Reactor) and event-driven / messaging & streaming familiarity
Financial services / wealth or alternatives domain experience
