Drive 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
Apply 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
Formal training or certification on software engineering concepts and advanced applied experience
Strong software engineering fundamentals — system design, data structures, algorithms, and architectural thinking — with the ability to ramp up quickly across varied tech stacks and project types
Hands-on practical experience in full-stack or cross-functional development, spanning frontend, backend, data pipelines, or mobile, with demonstrated ability to contribute across the length and breadth of a project
Proficiency in one or more of the following: Java/Spring Boot, Python/PySpark, GraphQL, or mobile development frameworks, with openness and aptitude to work across others as needed
Experience with cloud platforms (preferably AWS) and working knowledge of both relational and distributed data platforms (e.g., Oracle, Databricks)
Advanced understanding of agile methodologies, CI/CD pipelines, application resiliency, and secure software development practices
Proficient in all aspects of the Software Development Life Cycle, including design, development, testing, and operational stability in production environments
Practical understanding of AI/ML concepts and experience integrating or embedding AI/ML capabilities into business applications or data workflows, with the ability to identify opportunities where intelligent automation or predictive solutions can add business value
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
In-depth knowledge of the financial services industry and their IT systems
Preferred qualifications, capabilities, and skills
Experience with high-volume batch and real-time data processing using PySpark or similar frameworks, including performance tuning and troubleshooting of large-scale data pipelines
In-depth knowledge of relational and distributed databases (e.g., Oracle, Databricks) with hands-on experience in query optimization, PL/SQL, and shell scripting in a cloud-native AWS environment
Demonstrated ability to lead or contribute to architectural design discussions, technical evaluations, and proof-of-concept initiatives across diverse technology domains
Experience working in or alongside financial services technology teams, with an understanding of regulatory, compliance, and security considerations in enterprise-grade systems
