Applied AI ML Lead Engineer, VP - Asset and Wealth Management

We have an opportunity to impact your career and help you push the limits of what is possible. You will help teams adopt approved AI-assisted engineering practices that improve quality, speed, and operational resilience. If you enjoy building practical agentic tools and setting strong engineering standards, you will find meaningful ownership and growth here.
As an Applied AI ML Lead Engineer, Vice President in the Applied AI and Machine Learning engineering team within Asset and Wealth Management, you will enhance, build, and deliver trusted, market-leading technology products in a secure, stable, and scalable way. You will be a core technical contributor on an agile team, delivering critical technology solutions across multiple technical areas. You will help advance engineering outcomes by enabling responsible, compliant, and effective use of approved AI-assisted development practices.
Job Responsibilities
Drive adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes
Establish validation standards for AI-assisted work, including secure coding, peer review, and automated testing
Promote reuse of effective patterns across the team to improve consistency and reliability
Apply software development lifecycle tools, including approved AI-assisted development and automation capabilities, to increase the value realized from automation
Required Qualifications, Capabilities, and Skills
Experience leading effective use of approved AI-assisted software development tools for coding, code review, test acceleration, and troubleshooting
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, secure handling of inputs and outputs, and resiliency and security expectations
Experience coaching engineers on safe, compliant adoption of AI-assisted practices within delivery workflows
Knowledge of the financial services industry and its technology systems
Proficiency in Python or an equivalent programming language
Experience working in AWS or an equivalent cloud environment, with understanding of Terraform, EKS, and ECS
Strong knowledge of system and application design
Familiarity with CI/CD pipelines and software development lifecycles
Experience with prompt engineering and retrieval-augmented generation (RAG) based architecture
Ability to communicate with clarity and credibility across senior engineers, stakeholders, and product partners
Preferred Qualifications, Capabilities, and Skills
Experience building agentic tools and AI agents that support engineering workflows in production environments
Experience with Airflow