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Wealth Management (AI Residency)

Job Overview

Location

London

Job Type

Contract

Category

HR & Recruiting

Date Posted

April 11, 2026

Full Job Description

📋 Description

  • Wealth Management (AI Residency) at Mentis Technology Solutions Inc. offers senior financial professionals a unique opportunity to bridge institutional investment expertise with cutting-edge AI systems, shaping how AI reasons and makes decisions in high-stakes financial contexts.
  • Day-to-day responsibilities include developing and iterating realistic prompts to test AI-generated insights, comparing human versus machine judgment in asset allocation and financial planning, designing frameworks that translate expert portfolio construction into AI challenges, and constructing expert benchmarks using real-world portfolio analyses and advisory materials to evaluate frontier AI systems.
  • The team at Mentis AI combines deep asset management experience from firms like Lazard and Partners Group with machine learning and applied AI research, operating across London and San Francisco while collaborating with leading AI labs to advance AI’s role in wealth management.
  • Participants will gain hands-on experience applying advanced AI systems in wealth management, deepen their understanding of AI’s capabilities and limitations in financial advisory, contribute to pioneering research at the intersection of finance and AI, and build a portfolio of work that demonstrates leadership in AI-augmented investment decision-making.

Skills & Technologies

Remote

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About Mentis Technology Solutions Inc.

Mentis provides enterprise data-centric security solutions focused on dynamic data masking, sensitive data discovery, database activity monitoring, and compliance reporting. Founded in 2004, the company serves Fortune 500 organizations and government agencies worldwide, helping protect structured and unstructured data across relational databases, big data platforms, and mainframe environments through continuous classification, real-time masking, and risk analytics without altering source data or impacting performance.

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