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.
Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
Leads 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
Required Qualifications, Capabilities, and Skills
Formal training or certification on software engineering concepts and 5+ years applied software engineering experience
Strong knowledge of modern architectures and patterns, including microservices, REST APIs, event-driven design, and NoSQL data stores.
Experience building cloud-native applications and services; working knowledge of AWS (or comparable cloud platform).
Demonstrated experience delivering at least two large, complex applications end-to-end, from initial build through production delivery (ideally in a large financial institution or world-class product engineering organization).
Working knowledge of CI/CD and DevOps toolchains, observability/monitoring practices, and a test-driven approach within agile delivery models.
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
Advanced understanding of application resiliency and security principles and practices.
Strong technical documentation skills (e.g., API documentation with OpenAPI/Swagger). Familiarity with AI concepts and developer productivity tools (e.g., Microsoft Copilot or similar).
Demonstrated experience leading effective use of approved AI-assisted software development tools (coding, code review, test acceleration, troubleshooting) and setting clear team expectations for validating AI outputs for correctness, performance, and security.
Preferred Qualifications, Capabilities, and Skills
Experience designing and building high-availability system architectures.
Demonstrated ability to drive engineering process improvements and change adoption, including culture and ways-of-working changes.
