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