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About This Role
Vice President \- Artificial Intelligence
We are looking for a visionary Vice President of Artificial Intelligence (AI) to help us define, build, and drive Coastal’s AI strategy and roadmap across the organization. This is a build\-from\-the\-ground\-up leadership role with the opportunity to shape Coastal’s AI strategy, services, market positioning, delivery approach, and internal adoption.
As a key leader who will build and grow a team over time, you will focus on advancing Coastal as a premier consultancy for enterprise AI solutions. You will act as the face of our AI practice, drive market awareness through thought leadership, develop services in partnership with our marketing team, collaborate with our go\-to\-market (GTM) teams, take a leading role in advising clients, ensure solutions are delivered successfully, and collaborate with our IT team to optimize the AI tools internally as well
Role Responsibilities:
- Define Overall AI Strategy: Take ownership of a "blank white board" to architect, launch, and execute Coastal's comprehensive AI consulting strategy, practice structure, and AI services portfolio, including identifying new offerings, retiring outdated capabilities, prioritizing investments, and ensuring alignment with market demand and partner roadmaps.
- Market Leadership: Drive Coastal's strategic positioning and technical excellence as a leading consultancy for Anthropic, OpenAI, and Salesforce AI solutions, working closely with marketing, sales and delivery.
- Thought Leadership \& GTM Support: Serve as Coastal’s primary internal and external subject matter expert on AI; partner closely with marketing and GTM teams to develop collateral, publish insights, and build brand authority.
- Client\-Facing Pitching \& Advisory: Act as a strategic executive in client\-facing environments, pitching AI services to prospective clients and advising enterprise leaders on AI roadmaps.
- Establish Implementation Best Practices: Define frameworks and guardrails for deploying AI solutions responsibly and effectively, guiding delivery teams on implementation best practices.
- Practice Building \& Team Management: Recruit, onboard, and manage a high\-performing team of 2 to 5 direct reports in AI Engineer roles to support growing project pipelines.
- Company Enablement: Develop and lead ongoing Data \& AI enablement for sales, marketing, and delivery teams, ensuring employees understand Coastal's AI point of view, and evolving capabilities.
- Internal AI Tooling Oversight: Stay at the forefront of the rapidly evolving AI landscape to vet and recommend cutting\-edge tools for both internal operations and the creation of new services. Collaborate closely with the Delivery Excellence team to ensure Coastal leverages these technologies (such as Sweep.io) to maximize consultant productivity and drive continuous innovation.
- Partner Strategy: Work closely with our internal alliances and marketing strategists to develop relationships with our partners including Salesforce, OpenAI, and Anthropic.
Experience/Skills Required:
- Education: Bachelor's degree in Engineering, Computer Science, or a related field (Master’s preferred).
- Experience: 15\+ years of experience in a technical role, such as a solutions engineer, innovation engineer, or related field.
- Consulting Leadership: Proven background in management consulting or technology consulting, with a track record of advising enterprise\-level stakeholders.
- AI Subject Matter Expertise: Deep knowledge of modern AI frameworks, large language models (LLMs), and best practices for implementing scalable AI solutions.
- Platform Familiarity: Strong strategic and technical understanding of Anthropic, OpenAI, and Salesforce AI ecosystems.
- Broader Ecosystem Awareness: General technical awareness of data and AI capabilities within platforms like Snowflake, Databricks, and similar cloud data architectures.
- Entrepreneurial Mindset: Demonstrated capability to build a practice, service line, or corporate strategy from scratch with minimal initial structure.
- People Management: Experience hiring, mentoring, and directly managing technical teams (specifically software or AI engineers).
- Communication Excellence: Superb client\-facing presentation, pitching, and communication skills, with the ability to translate complex AI capabilities into clear business value.
- Travel Mobility: Ability and willingness to travel up to 50% for client pitches, site visits, and industry events.
- Work Authorization: Must have full\-time permanent US work authorization.
Additional Preferred Experience/Skills:
- Experience implementing Salesforce’s suite of platforms, such as CRM, Marketing Cloud, CDP/Data Cloud, and/or Tableau and CRMA.
- Hands\-on experience with code\-automation or AI productivity tools
- Previous experience working in a cross\-functional role that bridges technical and business teams.
Why Coastal, and what we offer:
- Flexible working hours with an emphasis on a life\-work balance (in that order!)
- Remote flexible work; “Live by the beach, work in the Cloud,” plus company office locations in Palm Coast, FL; Atlanta, GA; Tysons, VA \& Lexington, KY; travel as required to client locations
- Unlimited Paid Time Off (RTO), 401K with Company Match, and Medical, Vision, \& Dental coverage
- Competitive quarterly bonus opportunities
- Continuing education and certification reimbursements, specifically within the OpenAI, Anthropic, and Salesforce ecosystems; plus occasional in\-house competitions with spot bonuses
- A flexible and fun team culture! We value transparency, support, flexibility, growth, teamwork, fun, and so much more
- Frequent team and culture activities, virtual \& in\-person, including Lunch and Learns, Happy Hours, team\-building events
- Monthly All\-Hands calls to bring the company together, and an open\-door leadership policy with access to mentorship and guidance
- Opportunities for accelerated growth, networking, and career guidance and support
- Trust, transparency and respect across all levels of the company
*Coastal provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.*
*This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.*
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 Coastal, 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 $218,750 based on 3,817 positions with disclosed compensation.
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.
Coastal AI Hiring
Coastal has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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