Partner across product, operations, and engineering teams to align delivery plans, manage technical risk, and deliver measurable outcomes
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.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience
Advanced hands-on experience building microservices and APIs using Java (8+) and Spring Boot in large-scale environments
Strong full-stack engineering depth with modern web frameworks (React or Angular) and pragmatic API/UI design tradeoffs
Proven experience building and operating event-driven systems with Kafka, including reliability patterns (retries, idempotency, ordering, and dead-letter queues)
Solid database skills using Structured Query Language (SQL), including data modeling, query optimization, and performance tuning
Experience with NoSQL data stores (for example Cassandra, DynamoDB, or Elasticsearch), including data modeling for scale and reliability
Experience designing resilient, highly available architectures, including failure-mode analysis, recovery strategies, and secure engineering practices (authentication/authorization, secure APIs, data protection, dependency hygiene)
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
Preferred qualifications, capabilities, and skills
Experience building client-facing digital products in banking, lending, payments, or small-business platforms
Experience deploying and operating services on cloud platforms such as Amazon Web Services (AWS) and/or Cloud Foundry, including modern delivery pipelines
Experience with service reliability practices such as service-level objectives, capacity planning, and operational automation
Familiarity with Payment Card Industry Data Security Standard (PCI DSS) requirements and secure-by-design engineering practices
Relevant cloud or security certifications aligned to the role’s focus areas
