Foster an inclusive, collaborative team culture that values respect, learning, and shared ownership
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
Experience architecting, building, and supporting scalable, distributed applications in product-based and/or financial services environments
Experience leading large-scale modernization initiatives using microservices architecture across public cloud (Amazon Web Services) and hybrid environments
Hands-on experience designing and deploying Amazon Web Services solutions, including service selection, network fundamentals, and cloud security controls
Strong experience building, containerizing, and scaling microservices-based applications using AWS Lambda, Docker, and Kubernetes
Advanced proficiency in Java, Java Enterprise Edition (J2EE) frameworks, Spring Boot, Java Persistence API (JPA), and application integration using RESTful APIs and web services
Strong database experience with Oracle and/or PostgreSQL, including performance tuning and query optimization
Experience working with one or more NoSQL technologies such as MongoDB, Elasticsearch, Cassandra, or Neo4j
Experience with messaging/event streaming platforms such as Apache Kafka or TIBCO MQ
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
Experience delivering production use cases involving artificial intelligence and machine learning (AI/ML)
Experience with data warehousing and analytics technologies
Proficiency with modern front-end frameworks such as Angular and React
Domain knowledge within banking and financial services, including consumer lending and/or auto finance
