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Position: Director of AI Enablement
Department: Strategy
Reporting to: Chief Strategy Officer
Location: Topeka, KS and Lenexa, KS \- Onsite
Overview:Artificial Intelligence is changing the way businesses work—and at Advisors Excel, we're taking a thoughtful and reimagined approach to what comes next.
We're looking for a Director of AI Enablement to help shape how our employees, leaders, and financial advisor network understand and adopt AI. This is a unique opportunity to build an emerging capability from the ground up and help define how AI becomes a practical part of the way we work.
This isn't an AI engineering role. We're looking for someone who knows how to turn AI capabilities into business impact—someone who can educate, influence, build excitement, identify meaningful use cases, and help people confidently put AI to work.
You'll serve as the bridge between our business teams and Technology, helping Advisors Excel move from *"What can AI do?"* to reimagine *"How can AI help us do this better?"*
You won't be inheriting a finished AI program. You'll help build it. Join Advisors Excel and help shape what’s next!
What you’ll do:
- Build our AI enablement program from the ground up. Establish the framework, resources, training, communication, and processes that will support AI adoption across Advisors Excel.
- Make AI practical for our teams. Develop role\-based training, playbooks, prompt libraries, office hours, and other resources that help employees understand how to use AI effectively in their day\-to\-day work.
- Lead organizational change. Develop communication and engagement strategies that build understanding, address resistance, and reinforce new ways of working.
- Lead AI adoption. Guide the business through the adoption of Microsoft 365 Copilot and other approved AI tools, helping employees move beyond experimentation to meaningful, sustained use.
- Find where AI can make a difference. Partner with teams across the organization to understand their challenges, identify potential AI use cases, and prioritize opportunities based on business value and feasibility.
- Measure what matters. Establish and track AI adoption and value metrics—including utilization, active usage, time saved, and business impact—and communicate results to senior leadership.
- Build an AI community. Develop a network of AI champions and power users who can share best practices, support their peers, and help accelerate adoption across the organization.
- Connect business and technology. Partner closely with our Chief Technology Officer and Director of AI Technology to ensure business adoption and technical implementation move forward together.
- Champion responsible AI. Work with Information Security, Compliance, Legal, and other partners to help employees understand and follow AI governance, security, privacy, and responsible\-use expectations.
- Help bring AI to our advisor network. As our AI capabilities mature, support education and activation efforts that help financial advisors understand and leverage AI in their businesses.
What We’re Looking For:
We're looking for someone who has actually helped organizations adopt AI—not someone who simply wants to make AI their next career move.
- Five or more years of experience in technology enablement, AI adoption, or technology program management.
- Experience implementing or enabling AI productivity platforms such as Microsoft 365 Copilot, ChatGPT Enterprise, or comparable AI solutions.
- A strong understanding of AI capabilities, practical business applications, limitations, and responsible use.
- Exceptional communication and training skills, with the ability to make complex AI concepts understandable and practical for non\-technical audiences.
- Experience leading change, adoption, programs, and organizational communication.
- The ability to influence and collaborate across teams and levels of an organization without relying on direct authority.
- Comfort working in ambiguity and helping shape a developing AI strategy and operating model.
- A strategic mindset and the ability to connect AI opportunities to real business outcomes.
You’ll Stand Out If You Have:
- Experience in financial services, insurance, wealth management, or another highly regulated industry.
- Familiarity with responsible AI and AI governance frameworks, as well as enterprise data privacy, security, and risk considerations.
- A bachelor's degree in business, technology, communications, organizational development, or a related field.
What you’ll get:
- Amazing benefits including medical, dental, vision and 401k (with matching options)
- Generous PTO package from your start date
- Access to an on\-site café, gym and primary care
- Continuous personal and professional development opportunities
- Recognition for hard work \& exemplary performance
- Employee sponsored events…and more!
Who We Are:Advisors Excel is a fast paced, innovative company that seeks to service independent financial advisors in a multitude of areas within their business. From operational functions to sales and marketing, our end goal is to help continually grow advisors’ businesses nationwide. We thrive on watching them succeed. Not only does AE want to see the success of our advisors, but also our employees. We have high expectations from them to continually move our business forward. We are on the hunt for positive people who thrive on hard work and in a collaborative team environment. If this sounds like you, then what are you waiting for? We want you to join our team!
Advisors Excel is an Equal Employment Opportunity Employer. Everyone is welcome here – as an inclusive workplace, our employees are always comfortable bringing their true selves to our offices daily.
\#LI\-MS1
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Advisors Excel, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Advisors Excel AI Hiring
Advisors Excel has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Topeka, KS, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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