Director, Head Of Ai Engineering & Automation

Cresset Capital•Chicago, IL

13h

About The Position

Cresset is seeking a highly execution-oriented Head of AI Engineering & Automation to lead delivery, integration, and scaling of AI-powered solutions across the firm. The role operationalizes the firm’s AI strategy by translating prioritized initiatives into production-grade workflows, systems, and tools embedded in daily work. Reporting to the Chief Technology Officer and partnering closely with the Head of AI Strategy, this leader ensures AI initiatives are implemented, adopted, and optimized to deliver measurable business impact. The role works with Technology, Data, and business teams across Advisory, Investments, Client Service, Operations, and Marketing to build AI-enabled capabilities that improve efficiency, client experience, and firm-wide productivity. The Head of AI Engineering & Automation owns execution of AI priorities and serves as the bridge between strategy and implementation. The role provides feedback on feasibility, performance, and adoption to help refine the firm’s AI strategy. Success includes AI-powered workflows embedded across core functions, high adoption and sustained use, effective integration with enterprise systems, reduced manual processes, and measurable gains in productivity, scalability, and client experience.

Requirements

10+ years of experience in technology delivery, digital transformation, automation, or enterprise systems implementation.
Proven track record of delivering and scaling workflow, automation, or AI-driven solutions in complex organizations.
Strong hands-on experience with system integrations, APIs, workflow automation tools, and enterprise platforms.
Familiarity with AI tools and capabilities, including large language models, copilots, and intelligent automation.
Experience managing vendors, implementation partners, and cross-functional delivery teams.
Strong program and project management skills, with the ability to execute across multiple concurrent initiatives.
Ability to translate business requirements into technical solutions and deliver measurable outcomes.

Nice To Haves

Experience in financial services, wealth management, or a regulated industry.
Familiarity with enterprise platforms such as Salesforce, Snowflake, and Box, and workflow automation tools.
Experience with low-code/no-code platforms, RPA, or AI-enabled productivity tools.
Background in process engineering, systems architecture, or enterprise integration.

Responsibilities

Own end-to-end delivery of AI initiatives, from requirements gathering through deployment, scaling, and ongoing optimization.
Translate prioritized use cases and strategic objectives into implementation plans, technical designs, and deliverables.
Ensure AI solutions are production-ready, reliable, and aligned with enterprise architecture standards.
Analyze and redesign business processes to incorporate AI, automation, and intelligent workflows.
Identify opportunities to reduce manual effort, increase speed, and improve accuracy across core functions.
Build scalable frameworks and reusable components to accelerate automation across the organization.
Partner with Technology and Data teams to integrate AI capabilities into enterprise systems.
Oversee APIs, data pipelines, and system integrations supporting AI-enabled workflows, and ensure interoperability among internal systems and third-party platforms.
Partner with the Head of AI Strategy on vendor selection; lead implementation, configuration, and ongoing management of AI tools and platforms.
Manage external vendors and development partners, monitor performance, and optimize platform utilization.
Drive firm-wide adoption through training, onboarding, and embedded support; ensure solutions are intuitive, accessible, and aligned with user needs.
Partner with business leaders to embed AI into team workflows and performance expectations.
Own and maintain a prioritized execution roadmap aligned with the firm’s AI strategy; establish delivery standards, timelines, and governance.
Track and report adoption, performance, and ROI of deployed solutions and continuously improve outcomes.
Stay current on emerging AI tools, automation platforms, and best practices; test, iterate, and scale new capabilities.
Support integration of new capabilities into existing systems, including those related to M&A activities.
Provide feedback on feasibility, performance, and adoption to continuously refine the firm’s AI strategy.