Interested in this AI/ML Engineer role at CAPTRUST?
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Overview:
CAPTRUST is seeking a *Director of AI Enablement* to lead the adoption and scaling of artificial intelligence across the firm. This role is responsible for translating AI into measurable business outcomes by improving productivity, enhancing decision\-making, and embedding AI into advisor and client workflows.
The Director will own the firm's AI enablement strategy and execution, partnering with executive leadership and business teams to identify high\-value opportunities, deliver solutions, and drive adoption at scale.
This is a hands\-on leadership role focused on results, not strategy alone. Success will be measured by production deployments, enterprise adoption, and measurable improvements in productivity, speed, and quality.
The Director will establish and lead CAPTRUST's AI enablement function, defining the AI operating model, prioritizing investments, and building the team needed to scale impact across the firm.
Responsibilities:
- Own the firm's AI enablement strategy and execution, identifying and prioritizing high\-value use cases aligned to business objectives.
- Lead the end\-to\-end delivery of AI initiatives from concept through deployment, adoption, and scale.
- Partner with executive leadership to align priorities, drive decisions, and deliver measurable business outcomes.
- Oversee implementation of AI solutions, including vendor platforms and internally developed capabilities, and integrate them into core business workflows.
- Drive adoption through rollout plans, training, and stakeholder engagement.
- Establish repeatable processes, governance, and playbooks that enable AI to scale across the organization.
- Evaluate emerging AI technologies and vendors to inform roadmap, investment, and platform decisions.
- Identify opportunities to expand AI across advisor workflows, client experience, and internal operations.
- Build and lead the AI enablement function as adoption and business impact grow.
Qualifications:
*Minimum Qualifications:** Bachelor's degree or equivalent experience in a relevant field.
- 10\+ years of experience in technology, product, transformation, or related leadership roles, with ownership of enterprise\-scale initiatives.
*Desired Qualifications/Skills:** Proven track record deploying AI solutions into production environments and delivering measurable business outcomes.
- Experience implementing AI\-enabled capabilities such as copilots, agents, automation, or workflow\-integrated solutions.
- Experience leading enterprise AI enablement, digital transformation, or similar initiatives, including prioritization, deployment, adoption, and workflow integration across business functions.
- Demonstrated ability to translate business priorities into scalable technology solutions and drive execution from concept to adoption.
- Experience partnering with senior executives to align priorities, influence decisions, and deliver results.
- Strong execution, prioritization, stakeholder management, and change leadership skills in complex organizations.
- Working knowledge of data, security, governance, and risk considerations required to scale AI responsibly.
- Experience in financial services or other regulated industries strongly preferred.
WHAT can you expect from your career at CAPTRUST?
Our colleagues, like our clients, tend to stay with CAPTRUST for years. There’s a reason for it; it’s a great culture in which to work and grow. We all work together, each of us motivating those around us with our commitment to high standards. At CAPTRUST, expect a fully stocked break room, fun employee events, and a quality team surrounding you with opportunities for personal growth.
Our Employee Benefits Package shows how much we value our team. Some benefits include:* Company discretionary bonus
- Health, dental, and vision coverage, employer 401(k) plan and company match, health savings accounts, flexible spending accounts, and voluntary supplemental plans subject to plan terms
- Company\-paid benefits such as life insurance, short\-term disability, and long\-term disability, subject to applicable waiting periods.
- Paid time off (PTO) or Paid Sick Leave (PSL)
WHERE will you be working?
4208 Six Forks Rd \#1700 \| Raleigh, NC 27609
Due to the nature of the role, this is not a remote or work from home position. \#LI\-Onsite HOW do we build a world class organization one brick at a time?
We make it a priority to hire those who have a commitment to service, a real interest in other people, and a passion to continuously improve. Simply put: the difference at CAPTRUST is the quality of our people and depth of our bench. If you are ready to make your mark, we want to talk to you. *Are you the next brick?*
To get it done the CAPTRUST Way, an individual should exhibit the following characteristics:* Ability to build successful, collaborative, and trusting relationships
- Instinctive aptitude for consistently creating accurate, concise, respectful, and easy\-to\-understand verbal and written communications conveying complex information
- A strong sense of urgency about getting work done and solving problems to achieve results that benefit our clients and colleagues, even when faced with challenges
- Inherent desire to give back to our communities and enrich the lives of those around us
- An other\-centered mindset
- Integrity through maintaining objectivity
*EEO/Diversity Statement:*
At CAPTRUST, we are committed to building and maintaining a diverse workforce and inclusive work environment where ALL colleagues feel authentically seen, respected, and supported.
It is our intent to maintain a work environment that is free of harassment, discrimination, or retaliation because of sex (including pregnancy, childbirth, or other related medical conditions), gender, race (including hair texture or hairstyles associated with race), religion, color, national origin, ancestry, physical or mental disability, genetic information, age, sexual orientation, gender identity, gender expression, protected veteran status, uniformed service, or any other status protected by federal, state, or local laws. \#director
This position will remain open until filled.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At CAPTRUST, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
CAPTRUST AI Hiring
CAPTRUST has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Raleigh, NC, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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