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

Overview

In this VP role, you drive enterprise AI-enabled engineering practices to boost quality, speed, and resilience. You will be a core technical contributor within the Asset and Wealth Management AI/ML team, delivering trusted, scalable technology products. You’ll define validation standards and promote reusable patterns to raise engineering outcomes across the squad. This position offers ownership of impactful AI tooling and responsible adoption in a regulated financial services context.

Responsibilities

• Lead adoption of enterprise-approved AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes
• Establish validation standards for AI-enabled work, including secure coding, peer review, and automated testing
• Promote reuse of effective patterns across the team to improve consistency and reliability
• Apply SDLC tools, including AI-assisted development and automation, to maximize value from automation

Key requirements

• Experience leading effective use of 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/outputs, 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 with AWS or equivalent cloud environment, and 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 clearly with senior engineers, stakeholders, and product partners

• clear communication
• credibility with stakeholders
• leadership and coaching
• Python
• AWS
• Terraform

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