Leads evaluation sessions with external vendors, startups, and internal teams to assess architectural designs, technical capabilities, and applicability within existing systems and information architecture
Leads communities of practice across Software Engineering to drive awareness and adoption of new and leading-edge technologies
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
Formal training or certification on software engineering concepts and 5+ years of applied experience
Advanced hands-on experience with Python and delivering system design, application development, testing, and operational stability
Hands-on experience with artificial intelligence components and their application within modern technology stacks
Practical cloud-native experience, including foundational knowledge of AWS services and cloud engineering practices
Proficiency in automation and continuous delivery methods and all aspects of the Software Development Life Cycle
Advanced understanding of agile methodologies, CI/CD, application resiliency, and security
Demonstrated technical proficiency across modern software engineering disciplines such as cloud, artificial intelligence, and machine learning
Experience working with business and product partners to translate requirements into scalable technical solutions
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
Strong communication skills with the ability to collaborate effectively across technical, Product, and business team
Preferred qualifications, capabilities, and skills
Experience building scalable AI platforms for trading or financial services
Experience modernizing existing systems using AI-assisted engineering tools
Experience supporting electronic trading technology or other high-performance financial systems
Knowledge of Credit Markets, Sales and Trading, or related financial markets technology
Experience with AWS-based architectures in production environments
