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HARTMAN EXECUTIVE ADVISORS
Service Line Leader – AI \& Data
Hartman Executive Advisors (HEA) was founded to provide business\-focused, vendor\-independent leadership to mid\-sized companies. As independent, trusted strategic advisors, our mission is to create meaningful business outcomes that foster growth, greater financial returns, and market leadership. We help clients uncover the transformational opportunities that come from strategically aligned technology, advancing, cultivating, and leveraging people, processes, and systems to change and transform organizations.
Position Summary
We are seeking an entrepreneurial Service Line Leader – AI \& Data to grow and scale HEA's AI \& Data advisory practice. This is a hands\-on leadership role built around service definition and delivery. The successful candidate will be the subject\-matter authority who scopes, estimates, and closes AI \& Data engagements sourced by HEA's BD/Marketing team and account leaders, build the delivery methodology and team, and serve as HEA's public face and thought leader for AI \& Data in the market.
Reporting \& Organizational Fit
- Reports to the COO
- Serves as a peer to HEA's existing Service Line Leaders
- Partners directly with HEA's four industry vertical leads (Financial Services; Healthcare \& Human Services; Nonprofit \& Education; Supply Chain, Construction \& Real Estate), who surface AI \& Data opportunities within their existing client accounts
- Partners with the CRO and BD/Marketing team, who own new\-logo lead generation and pipeline sourcing for AI \& Data
Key Responsibilities
1\. Deal Estimating, Solutioning \& Closing
- Serve as the AI \& Data subject\-matter expert in active sales cycles sourced by BD/Marketing (new clients) and account leaders/account teams (existing clients)
- Lead solution scoping, estimating, and proposal development for AI \& Data engagements, translating client needs into credible statements of work and pricing
- Personally participate in and help close deals, bringing technical credibility and executive presence to the table alongside account leaders and BD
- Lead contract and commercial negotiation for AI \& Data engagements
- Own revenue, margin, and growth targets for the service line, with accountability for estimating accuracy, win rate, and deal profitability
2\. Market Presence \& Thought Leadership
- Serve as the face of AI \& Data for HEA in the market, the go\-to public voice, in partnership with Marketing, on AI \& Data topics
- Represent HEA at industry forums, conferences, webinars, and in publications as an AI \& Data subject\-matter expert
- Partner with Marketing on content, point\-of\-view pieces, and campaigns that build HEA's AI \& Data brand and credibility ahead of the sales cycle
- Translate complex AI \& Data concepts into clear, actionable insight for non\-technical executive audiences, both externally and internally
3\. Practice Building \& Delivery
- Build, launch, and scale a profitable AI \& Data service line, including staffing model, pricing strategy, and service catalog
- Recruit, vet, and manage a bench of fractional AI \& Data Associates consistent with HEA's fractional delivery model
- Create repeatable delivery methodologies, accelerators, and reusable intellectual property
- Collaborate with business and technical teams to define, prioritize, and roadmap client AI \& Data initiatives
4\. Advisory \& Innovation
- Advise HEA leadership and clients on AI \& Data strategy, market trends, and emerging opportunities
- Evaluate emerging AI technologies, vendors, and partners, maintaining HEA's vendor\-independent posture, no single\-vendor bias in recommendations to clients
5\. Responsible AI \& Risk Oversight
- Apply right\-sized, pragmatic AI governance and risk practices appropriate to mid\-market clients, prioritizing practical guardrails over enterprise\-scale compliance frameworks
- Identify risks associated with client AI adoption and recommend proportionate mitigation strategies
- Periodically assess delivered AI \& Data engagements for business impact, scalability, and ROI
Qualifications
- 5\+ years of experience in AI/ML, data science, or related fields, including demonstrated advisory or strategic leadership contributions
- Proven track record scoping, estimating, and closing complex consulting or professional services engagements as the subject\-matter expert in the sales cycle. Comfortable being brought in by account leaders and BD to help win deals
- Established or emerging public profile as an AI \& Data thought leader, speaking, writing, or media experience is a strong plus
- Strong working knowledge of AI technologies including machine learning, NLP, computer vision, and generative AI/LLMs
- Working knowledge of AI governance and responsible AI practices, applied pragmatically for mid\-market clients
- Strong business acumen with a track record of aligning technology strategy to business outcomes
- Exceptional communication, executive presence, and stakeholder management skills
- Comfort operating in an advisory delivery model rather than a traditional staffed\-team consulting environment
- Bachelor's degree in a related field required; Master's degree preferred but not required
Job Type: Full\-Time
Benefits: Medical, Dental, Vision, 401k with Match
Salary Context
This $175K-$250K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Hartman Executive Advisors, 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. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $175K to $250K.
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.
Hartman Executive Advisors AI Hiring
Hartman Executive Advisors has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Baltimore, MD, US. Compensation range: $250K - $250K.
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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