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About This Role
About Cresset
Cresset is a firm built by clients, for clients. As an independent, award-winning multi-family office and private investment firm, we are reimagining the way wealth is experienced. Our purpose is to help ensure that both wealth and life are fully optimized**—integrated, intentional, and aligned with each client’s vision of success.
We provide access to the caliber of talent, ideas, and investment opportunities typically available to the largest single-family offices and institutions. Our approach is personalized, entrepreneurial, and client-first.
Cresset was built to endure. We are creating a 100+ year firm—one focused on delivering an exceptional experience, not only for the families we serve but for the team that serves them. Recognized by Barron’s among the nation’s top multi-family offices,* Cresset is guided by long-term relationships, shared success, and a belief that wealth should serve a life well lived.
Job Description
Cresset is seeking a highly execution-oriented Head of AI Engineering & Automation to lead the delivery, integration, and scaling of AI-powered solutions across the firm.
This role is responsible for operationalizing the firm’s AI strategy—translating prioritized initiatives into production-grade workflows, systems, and tools that are embedded into how teams work every day. Reporting to the Chief Technology Officer and working in close partnership with the Head of AI Strategy, this leader ensures that AI initiatives are effectively implemented, adopted, and optimized to deliver measurable business impact.
You will work closely with Technology, Data, and business teams across Advisory, Investments, Client Service, Operations, and Marketing to design, build, and scale AI-enabled capabilities that improve efficiency, enhance client experience, and drive firm-wide productivity.
Key Responsibilities
AI Solution Delivery & Execution
- 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 actionable implementation plans, technical designs, and deliverables.
- Ensure AI solutions are production-ready, reliable, and aligned with enterprise architecture standards.
Workflow Automation & Process Transformation
- 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.
Technology Integration & Architecture
- Partner with Technology and Data teams to integrate AI capabilities into enterprise systems.
- Oversee APIs, data pipelines, and system integrations required to support AI-enabled workflows.
- Ensure seamless interoperability across internally built systems and third-party platforms.
Vendor Implementation & Management
- Partner with the Head of AI Strategy on vendor selection, then lead implementation, configuration, and ongoing management of AI tools and platforms.
- Manage relationships with external vendors and development partners to ensure timely, high-quality delivery.
- Monitor vendor performance and optimize platform utilization to maximize value.
Adoption, Enablement & Change Management
- Drive firm-wide adoption of AI tools and workflows through training, onboarding, and embedded support.
- Ensure solutions are intuitive, accessible, and aligned with user needs to maximize engagement and usage.
- Partner with business leaders to embed AI into team workflows and performance expectations.
Program Delivery & Performance Management
- Own and maintain a clear, prioritized execution roadmap aligned with the firm’s AI strategy.
- Establish delivery standards, timelines, and governance for AI and automation initiatives.
- Track and report on adoption, performance, and ROI of deployed solutions, continuously improving outcomes.
Continuous Improvement & Innovation
- Stay current on emerging AI tools, automation platforms, and best practices.
- Rapidly test, iterate, and scale new capabilities to enhance the firm’s AI ecosystem.
- Support integration of new capabilities into existing systems, including those related to M&A activities.
Partnership with Head of AI Strategy
- The Head of AI Strategy defines the vision, priorities, and roadmap for AI across the firm.
- The Head of AI Automation & Enablement owns execution—ensuring those priorities are successfully delivered, adopted, and scaled.
This role serves as the critical bridge between strategy and implementation, providing feedback on feasibility, performance, and adoption to continuously refine the firm’s AI strategy.
Qualifications
- 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.
Preferred Experience
- Experience in financial services, wealth management, or a regulated industry.
- Familiarity with enterprise platforms such as Salesforce, Snowflake, 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.
What Success Looks Like
- AI-powered workflows embedded across core business functions
- High adoption and sustained usage of AI tools across teams
- Seamless integration of AI capabilities into enterprise systems
- Significant reduction in manual processes and operational inefficiencies
- Measurable improvements in productivity, scalability, and client experience
What We Offer:
At Cresset, we focus on people first. As a service business, our people are our assets. Engaging our clients and employees is our highest priority. Starting salary is $175,000 - $200,000. Salary will be based on factors including, but not limited to, experience, licenses/certifications, industry knowledge, and geographic location. Cresset offers a competitive compensation package including an annual incentive and a benefits package to all full-time employees including medical, dental, vision, life insurance, 401(k) retirement plan, health savings accounts, short and long-term disability insurance, voluntary critical and accident insurance, and pre-tax parking and transportation programs.
Equal Employment Opportunity
It is the policy of Cresset to ensure equal employment opportunity (EEO) for all employees and applicants for employment without regard to race, color, religion, sex, pregnancy (including childbirth, lactation, or related conditions), national origin or ancestry, age, disability, veteran status, uniformed servicemember status, sexual orientation, gender identity, status as a parent, genetic information (including testing and characteristics), or any other characteristic protected by applicable federal, state, or local law. It is Cresset’s policy to comply with applicable laws concerning the employment of persons with disabilities, including reasonable accommodation for applicants and employees with disabilities.
Everything Starts With Culture.
Our Vision
We exist to optimize wealth and elevate life—giving clients the time, clarity, and trusted partnership to pursue what matters most.
Our Values
- Disclosures related to awards, recognitions, and rankings available here.
- Barron's Top 100 RIA Firms (issued 09.2026). Based on data from July of the prior year through June of the award year. Rankings are determined using a proprietary methodology that considers qualitative and quantitative factors. Cresset paid a licensing fee to use the Barron's award logos.
**Wealth Optimized, Life Elevated refers to the firm’s philosophy and process in providing advisory and planning services and is not intended to convey a guarantee of results.
Cresset refers to Cresset Capital Management, LLC and its respective direct and indirect subsidiaries and controlled affiliates. For a full list of Cresset subsidiaries and controlled affiliates, please see cressetcapital.com/disclosures/.
Salary Context
This $175K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Cresset, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,040 based on 13,780 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($187K) sits 12% below the category median. Disclosed range: $175K to $200K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Cresset AI Hiring
Cresset has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US. Compensation range: $200K - $200K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (140) are outnumbered by mid-level (5,630) and senior (4,176) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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