- Designs, codes, tests, and delivers automation (including LLMs/agents) to
eliminate manual operational work and streamline AO workstreams’ remediations (e.g., control items, security vulnerabilities, upgrades, and FARM findings).
- Governs application risk, controls, and compliance: owns adherence to firm
standards, partners with Technology Risk & Controls, manages Technology Lifecycle Management (TLM), and drives closure of issues/findings (e.g., FARM) through effective remediation and evidence management.
- Owns security and data accountability for the application: ensures strong
authentication/authorization, vulnerability and certificate hygiene, and proper data registration/classification plus compliant storage, retention, and disposal.
- Coordinates across product and engineering at scale to prioritize and drive
execution with a clear sense of urgency for AO workstreams, including influencing/coaching teams and aligning execution across large developer communities.
- Runs resilient, well-operated production services end-to-end: implements
monitoring/logging and anomaly detection, maintains secure network configurations/least privilege, and leads/supports incident, problem, and change management and recovery/resiliency readiness.
- Demonstrates and champions site reliability culture and practices and exerts
technical influence throughout the team.
- Leads initiatives to improve the reliability and stability of applications
and platforms using data-driven analytics to improve service levels.
- Collaborates with team members to identify comprehensive service level
indicators and stakeholders to establish reasonable service level objectives and error budgets with customers.
- Documents and shares knowledge within the organization via internal forums
and communities of practice.
- 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.