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About This Role
At Oceans Healthcare, we are passionate about helping adults and seniors attain the best possible quality of life. As a nationally recognized provider of behavioral health services, we treat patients experiencing symptoms of depression, anxiety, schizophrenia, behavioral changes related to medication management or substance abuse and other behavioral issues.
Through our inpatient behavioral services and intensive outpatient programs, Oceans Healthcare offers comprehensive behavioral and mental health services to help patients at every stage of the healing process. Our staff is committed to caring for patients and their families with dignity, honesty and compassion.
The Director, Enterprise AI \& Innovation is responsible for leading Oceans Healthcare's enterprise artificial intelligence strategy while personally driving the design, development, implementation, and adoption of AI\-enabled solutions across the organization.
Reporting directly to the Chief Executive Officer, this executive will serve as both a strategic advisor and hands\-on builder, translating emerging AI capabilities into practical applications that improve patient care, workforce productivity, clinical quality, operational efficiency, financial performance, and decision\-making. This leader will partner closely with executive leadership, clinical operations, finance, revenue cycle, human resources, compliance, legal, information technology, and business leaders to identify high\-value opportunities where artificial intelligence can transform the way Oceans Healthcare delivers behavioral healthcare services.
As Oceans Healthcare's senior AI executive, the Director will establish a long\-term AI vision, create an enterprise AI roadmap, build an AI Center of Excellence, and position Oceans Healthcare as the national leader in the responsible application of artificial intelligence within behavioral healthcare.
Essential Functions:
- Develop and execute the organization's enterprise artificial intelligence strategy in alignment with Oceans Healthcare's strategic priorities; Lead the planning, execution, and ongoing management of enterprise AI initiatives from concept through implementation and optimization.
- Identify opportunities where AI and automation can improve patient outcomes, operational efficiency, workforce productivity, and financial performance.
- Establish a multi\-year roadmap for AI adoption across hospitals, outpatient programs, corporate operations, and shared services.
- Advise executive leadership regarding emerging AI technologies, industry trends, risks, and implementation strategies.
- Prioritize initiatives based on organizational value, operational readiness, regulatory considerations, and return on investment.
- Collaborate with project management and operational leaders to ensure projects remain on schedule, within scope, and aligned with business objectives.
- Develop policies, standards, and governance processes for the responsible use of artificial intelligence throughout the organization.
- Ensure AI initiatives comply with HIPAA, CMS Conditions of Participation, Joint Commission standards, state regulations, and applicable privacy requirements.
- Partner with Compliance, Legal, Information Security, and Privacy teams to evaluate technology risks and establish appropriate safeguards.
- Oversee processes for AI model validation, monitoring, transparency, and ongoing performance evaluation;
- Promote ethical and responsible use of AI technologies across the enterprise.
- Establish key performance indicators for enterprise AI initiatives.
- Measure operational, financial, and clinical outcomes associated with implemented solutions; Present program updates, implementation progress, and performance metrics to executive leadership.
- Identify opportunities for continuous improvement and expansion of successful AI initiatives.
- Other duties and responsibilities as assigned.
Requirements
Education / Experience:
- Bachelor's degree in Computer Science, Information Technology, Healthcare Informatics, Data Analytics, Engineering, Business Administration, or a related discipline. Master’s degree preferred.
- Minimum of eight (8\) years of progressive leadership experience in information technology, analytics, digital transformation, or enterprise technology.
- Minimum of five (5\) years leading large\-scale technology or AI initiatives across multiple departments.
- Experience implementing enterprise software solutions in complex or highly regulated industries.
- Demonstrated ability to lead cross\-functional teams and influence executive decision\-making.
- Experience managing organizational change and technology adoption initiatives.
- *Microsoft Ecosystem Experience:* Microsoft 365 Copilot, Copilot Studio, Power Platform, Power Automate, SharePoint, Teams, Entra ID, Microsoft Fabric, Azure AI Search.
- *Cybersecurity, Privacy, \& Responsible AI:* HIPAA, HITECH, PHI protection, AI risk management, prompt injection protection, model monitoring, audit logging, explainability, fairness, transparency, accountability, and human oversight.
Skills / Abilities:
- Strong understanding of artificial intelligence, automation, machine learning, and emerging digital technologies.
- Knowledge of healthcare privacy, security, and regulatory requirements related to technology implementation.
- Exceptional strategic planning and organizational leadership skills.
- Ability to communicate complex technical concepts to executive leadership and non\-technical audiences.
- Strong project and program management capabilities.
- Excellent analytical, problem\-solving, and decision\-making skills.
- Ability to balance innovation with operational practicality and regulatory compliance.
- Strong relationship\-building and collaboration skills across diverse stakeholder groups.
- Demonstrated commitment to continuous learning and emerging technologies.
Work Environment:
Subject to many interruptions. Occasional pressure due to multiple calls and inquiries. This position can be high paced and stressful; must be able to cope mentally and physically to atmosphere. Work requires spending approximately 90% or more of the time inside a building that offers protection from weather conditions but not necessarily from temperature changes. Travel required, including via automobile, planes and/or trains, as required.
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 OCEANS HEALTHCARE, 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. 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.
OCEANS HEALTHCARE AI Hiring
OCEANS HEALTHCARE has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Plano, TX, 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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