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About This Role
Director of AI & Automation
By the Sea Resorts
Panama City Beach, FL
Full-Time
Position Overview
By the Sea Resorts is seeking a highly capable, hands-on Director of AI & Automation to lead the practical adoption of artificial intelligence, automation, and emerging technologies across our growing organization.
This position combines AI strategy with hands-on technical execution. We are looking for someone who can determine where AI can create meaningful business value and then personally design, develop, integrate, test, and deploy the solutions needed to make it happen.
This is not a purely advisory, consulting, or AI architecture role. The successful candidate will be expected to write code, build integrations, work with APIs and databases, develop AI applications and automated workflows, troubleshoot technical problems, and take solutions from concept through production deployment.
By the Sea Resorts operates a diverse portfolio of hotels, restaurants, employee housing, and commercial laundry facilities. The Director of AI & Automation will work directly with company leadership and operational teams across Operations, Human Resources, Finance, Sales, Marketing, Revenue Management, IT, and Commercial Laundry.
The ideal candidate combines the vision to identify how AI can transform a business with the technical ability to personally build the systems that make that transformation possible.
Key Responsibilities
- Develop and lead the company's long-term AI, automation, and digital transformation strategy.
- Identify high-value opportunities for AI, workflow automation, system integration, and process improvement throughout the organization.
- Personally design, code, build, test, deploy, and maintain AI-powered applications, internal tools, assistants, agents, and automated workflows.
- Build production-ready solutions using large language models and AI platforms such as OpenAI, Microsoft, Google, Anthropic, or similar technologies.
- Develop AI agents, retrieval-augmented generation (RAG) systems, internal knowledge assistants, enterprise search tools, and decision-support applications.
- Integrate AI and automation solutions with existing company systems using APIs, databases, webhooks, middleware, and integration platforms.
- Work with property management systems, reservation systems, point-of-sale systems, accounting platforms, HR systems, operational systems, and other enterprise technology.
- Build and maintain data pipelines and integrations necessary to support AI applications and automated business processes.
- Develop internal applications and tools using Python, SQL, APIs, and other appropriate technologies.
- Partner directly with executives, department leaders, hotel teams, front-line employees, call centers, plant managers, and other operators to understand business challenges.
- Translate operational problems into practical technical solutions.
- Create prototypes when appropriate, but take successful solutions beyond experimentation into reliable production systems employees actually use.
- Evaluate third-party AI and automation products and determine when the organization should build internally versus purchase or integrate an outside platform.
- Create AI-powered reporting, analytics, forecasting, dashboards, and decision-support tools.
- Automate repetitive administrative and operational processes while maintaining appropriate controls and human oversight.
- Establish standards for AI security, data privacy, access control, responsible AI use, and governance in partnership with IT and executive leadership.
- Test AI systems for accuracy, security, reliability, scalability, and usability before deployment.
- Monitor deployed solutions and continuously improve performance based on user feedback, system performance, and measurable business results.
- Document applications, integrations, workflows, architecture, APIs, data flows, and technical processes.
- Train employees and leaders on newly implemented AI tools and systems.
- Manage relationships with outside AI, software, and technology vendors when external expertise is needed.
- Stay current on emerging AI technologies and recommend technologies that can create measurable operational or financial value.
- Establish priorities and maintain an AI and automation roadmap based on business impact, complexity, cost, and expected return on investment.
Potential Areas of Focus
Hotel Operations
Guest communications, review analysis, service recovery, operational reporting, housekeeping productivity, maintenance tracking, labor forecasting, property-level reporting, and workflow automation.
Human Resources
Recruiting automation, applicant screening support, onboarding, employee communications, policy knowledge tools, workforce analytics, HR reporting, document automation, and employee self-service tools.
Finance
Reporting automation, invoice processing, financial analysis, variance identification, data consolidation, forecasting, anomaly detection, and management reporting.
Sales & Marketing
Lead management, customer segmentation, content development, campaign analysis, sales reporting, automated follow-up, and customer insights.
Revenue Management
Reporting automation, competitive analysis, forecasting support, pricing insights, trend identification, and data consolidation.
Commercial Laundry
Production analytics, labor forecasting, logistics and route optimization, equipment and maintenance analytics, operational dashboards, production scheduling, and plant performance reporting.
Executive Leadership
Automated KPI reporting, business intelligence, executive dashboards, trend identification, forecasting, operational alerts, and decision-support tools.
Required Qualifications
- Bachelor's degree in Computer Science, Information Technology, Data Science, Engineering, Business Technology, or a related field, or equivalent professional experience.
- 4+ years of experience in software development, AI engineering, automation engineering, systems integration, data engineering, or a closely related technical field.
- Demonstrated experience personally building and deploying production software or AI solutions.
- Hands-on experience developing applications using generative AI and large language model APIs.
- Experience deploying production integrations involving LLM APIs, AI agents, or RAG systems, rather than only proofs of concept or demonstrations.
- Strong proficiency with Python.
- Strong working knowledge of SQL and relational databases.
- Experience designing, consuming, and integrating REST APIs, webhooks, databases, and enterprise systems.
- Experience integrating multiple business applications and data sources.
- Experience building automated workflows and backend services.
- Understanding of software development practices including version control, testing, debugging, deployment, documentation, and ongoing maintenance.
- Strong understanding of AI capabilities, limitations, hallucination risks, data privacy, security, and responsible AI practices.
- Ability to understand complex business processes and translate them into practical technical solutions.
- Strong troubleshooting and problem-solving skills.
- Ability to independently manage projects from initial business problem through development and production deployment.
- Ability to work directly with non-technical operators and department leaders to understand how work actually gets done.
- Strong written and verbal communication skills with the ability to explain technical concepts to non-technical audiences.
Preferred Qualifications
- Experience with OpenAI APIs, Azure OpenAI, Microsoft Copilot, Google Gemini, Anthropic Claude, or similar AI platforms.
- Experience building AI agents, RAG systems, internal knowledge assistants, enterprise search tools, or AI-enabled business applications.
- Experience with automation platforms such as Microsoft Power Automate, Zapier, Make, n8n, or similar technologies.
- Experience with cloud platforms such as Microsoft Azure, AWS, or Google Cloud.
- Experience with data visualization, business intelligence platforms, dashboards, and reporting tools.
- Experience with Microsoft 365 environments and enterprise system integrations.
- Experience integrating systems such as property management systems (PMS), central reservation systems (CRS), point-of-sale systems (POS), ERP/accounting platforms, CRM systems, HRIS platforms, or operational management systems.
- Experience implementing technology in hospitality, restaurants, logistics, manufacturing, commercial laundry, multi-location operations, or another operationally complex environment.
- Experience building applications used by front-line employees rather than only technical users.
- Experience managing outside technology vendors, development partners, or implementation consultants.
- Knowledge of cybersecurity, identity management, data governance, and AI governance principles.
What We Will Ask Candidates to Demonstrate
Candidates should be prepared to discuss specific AI, automation, software, or integration projects they personally built.
Strong candidates will be able to explain:
- What business problem they were solving.
- What they personally designed and developed.
- What programming languages, APIs, databases, AI models, and platforms they used.
- How the solution was integrated with existing business systems.
- How the solution moved from prototype into production.
- How many employees or customers actually used the solution.
- What technical challenges they encountered and how they solved them
- What measurable business results the project produced.
A portfolio, GitHub repository, technical project examples, or other demonstrations of previously developed solutions are strongly preferred.
What Success Looks Like
The successful candidate will move the company beyond AI experimentation and into practical, production-level implementation.
Success in this position will include:
- Creating a clear and actionable AI and automation roadmap for the organization.
- Personally building and deploying AI applications and automated workflows.
- Implementing solutions employees actually adopt and use.
- Reducing repetitive administrative and operational work.
- Improving access to company information and operational data.
- Connecting previously disconnected systems and data sources.
- Improving employee productivity and decision-making.
- Delivering measurable financial or operational return on AI and technology investments.
- Creating a scalable technology foundation that allows the company's use of AI and automation to grow over time.
This individual should be equally comfortable discussing AI strategy with executive leadership and opening a development environment to build the solution themselves.
About By the Sea Resorts
By the Sea Resorts is a growing hospitality and operations company headquartered in Panama City Beach, Florida. Our portfolio includes hotels, restaurants, employee housing, and commercial laundry operations serving multiple markets throughout Florida and Alabama.
As our organization continues to grow, we are investing in technology that allows our teams to work more efficiently, make better decisions, reduce unnecessary manual work, and provide exceptional service.
The Director of AI & Automation will have the opportunity to shape how artificial intelligence and automation are used throughout the organization while personally building many of the systems that drive that transformation.
By the Sea Resorts is an Equal Opportunity Employer. We are committed to providing employment opportunities without regard to race, color, religion, sex, national origin, age, disability, veteran status, or any other status protected by applicable law.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At By The Sea Resorts, 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
By The Sea Resorts AI Hiring
By The Sea Resorts has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Panama City Beach, FL, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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