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About This Role
About Anthony & Sylvan
Looking to build something that lasts—literally? At Anthony & Sylvan Pools, your skills create real impact. For nearly 80 years, we’ve been turning backyard dreams into reality as the nation’s largest and most recognizable name in swimming pools. Founded in 1946, we design and build quality inground pools across the U.S., while also offering renovations, modernizations, and ongoing maintenance services. We’re growing fast and seeking candidates who take pride in craftsmanship, value teamwork and safety, and are ready to grow with us. Learn more at anthonysylvan.com.
About the Role
Anthony & Sylvan Pools is seeking an AI Solutions Developer to join our technology team and help build the next generation of AI-enabled business solutions.
This role is designed for an early-career technologist with strong programming skills and hands-on experience building with artificial intelligence. We are particularly interested in recent graduates who have developed meaningful technical experience through internships, co-ops, research, capstone projects, or substantial independent projects.
General Summary of Position
This is not a traditional entry-level development role focused on maintaining existing applications. You will have the opportunity to work directly on real business problems and build AI-powered applications, agents, automations, and integrations that are put into production and used across the company.
The ideal candidate enjoys experimenting with emerging technology, learns quickly, and is equally interested in understanding what problem should be solved as they are in writing the code to solve it.
Essential Functions and Responsibilities
- Design, develop, and implement AI-powered applications, agents, workflows, and intelligent automations that address real business needs and improve customer experience, employee productivity, and operational efficiency.
- Partner with business and technology teams to understand processes and requirements, identify opportunities for AI and automation, rapidly prototype potential solutions, and develop successful concepts into scalable, production-ready applications.
- Develop AI-enabled applications using Python and other modern development technologies, leveraging large language models (LLMs), generative AI platforms, APIs, AI development frameworks, and structured and unstructured data.
- Build agentic and intelligent workflows using techniques such as prompt engineering, retrieval-augmented generation (RAG), tool/function calling, and AI orchestration to enable solutions that can reason, take actions, interact with systems, and automate multi-step business processes.
- Integrate AI applications with enterprise systems and data sources including CRM, ERP, data and communication platforms, and other business applications by building and consuming REST APIs, web services, and related integrations.
- Support the full solution development lifecycle including solution design, testing, deployment, monitoring, evaluation, and continuous improvement, with a focus on improving AI output reliability, accuracy, performance, scalability, and user experience.
- Explore emerging AI technologies and development approaches to identify practical business applications, while maintaining clear technical documentation, sharing knowledge with the technology team, and contributing to the continuous improvement of AI development practices.
- Other duties as assigned
*QUALIFICATIONS – Knowledge, Skills & Abilitie*s
Education
Bachelor’s degree in Artificial Intelligence, Computer Science, Software Engineering, Data Science, Information Sciences & Technology, Computer Engineering, or a related technical discipline
Licenses/Certifications
None required
Years of Experience
Demonstrated hands-on experience developing AI, machine learning, or software solutions. Relevant experience may include professional roles, internships, co-ops, research, capstone projects, freelance development, open-source contributions, hackathons, or substantial independent projects. Demonstrated capability and quality of work are valued over a specific number of years of experience.
Required Skills & Abilities
Strong programming skills, particularly in Python; experience with JavaScript, TypeScript, C#, or similar languages is valuable
Hands-on experience developing AI or machine learning solutions and working with modern generative AI models and APIs
Strong understanding of software development fundamentals, data structures, APIs, databases, and application architecture
Familiarity with SQL and structured data
Strong analytical and problem-solving skills with the ability to quickly learn and apply new technologies
Ability to communicate technical concepts effectively and collaborate with both technical and non-technical stakeholders
Curious, experimental mindset with a willingness to iterate, test new approaches, and challenge traditional solutions
Experience with one or more of the following preferred: LLM platforms (OpenAI, Azure OpenAI, Anthropic, Google Gemini); agentic AI and multi-step workflows; RAG, embeddings, vector databases, or semantic search; AI evaluation, guardrails, observability, or responsible AI practices; API development and systems integration
Experience with cloud and development technologies such as Microsoft Azure, Microsoft Fabric, GitHub, GitHub Copilot, data engineering, or analytics platforms preferred
Exposure to CRM, ERP, contact center, workflow automation, or other enterprise business applications preferred
Demonstrated experience building and deploying complete applications or technical solutions beyond basic classroom assignments preferred
Travel Requirements
N/A or As needed
Other Requirements
Candidates are encouraged to provide examples of technical work, such as a GitHub repository, portfolio, capstone project, AI agent, application, research project, hackathon submission, or other relevant project.
PHYSICAL REQUIREMENTS
- Primarily sedentary work performed in an office environment
- Ability to communicate promptly and clearly via phone and computer
ACKNOWLEDGEMENT
Position duties and expectations change over time. This job description is not intended to be an all-inclusive, exclusive or exhaustive list of the job functions that an employee in this position may be asked to perform from time to time. The employee may perform other related duties as assigned to meet the ongoing needs of the Company. If there is ever a time that you are unsure what is expected of you, you are expected to meet with your manager and ask questions. If you are unsure how to perform a certain task, ask your manager before performing.
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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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Anthony & Sylvan Pools, 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 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Anthony & Sylvan Pools AI Hiring
Anthony & Sylvan Pools has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Warminster, PA, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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