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About This Role
Job Description: AI Website Developer
Position Overview
We are seeking an innovative, tech-savvy AI Website Developer to build, maintain, and continuously improve websites using AI-powered development tools and workflows. This role will leverage existing AI platforms, frameworks, and processes to deliver high-quality websites efficiently while helping develop new AI agents that automate website creation, streamline internal workflows, and improve the client experience.
The ideal candidate combines website development knowledge with a strong understanding of AI tools, automation, and emerging technologies. This individual will play a key role in maintaining and advancing our AI website-building capabilities, identifying opportunities for improvement, and helping shape the next generation of AI-driven website development.
Key Responsibilities
- AI-Powered Website Development: Build, launch, maintain, and enhance websites using existing AI tools, platforms, frameworks, and development processes.
- AI Agent Development: Help design, build, test, and refine AI agents that automate website creation, improve development workflows, and support client interactions.
- Platform Improvement: Maintain and continuously improve our AI website-building systems, processes, and capabilities to increase efficiency, consistency, quality, and scalability.
- Website Maintenance and Optimization: Troubleshoot issues, implement updates, improve website performance, and ensure sites are functional, responsive, secure, and user-friendly.
- Client Interface and Experience: Help develop AI-powered processes and agents that gather client requirements, guide clients through website development, answer questions, and improve communication throughout the project lifecycle.
- Workflow Automation: Identify repetitive tasks and opportunities to use AI, integrations, APIs, and automation to reduce manual work and accelerate website delivery.
- Quality Assurance: Review AI-generated websites and code for accuracy, functionality, design quality, usability, and technical performance.
- Continuous Learning: Stay current with advancements in AI development tools, website technologies, automation platforms, and agent-based systems; recommend and implement improvements that provide practical business value.
- Collaboration: Work with team members and clients to understand project requirements, resolve challenges, and deliver effective website solutions.
Qualifications and Skills
- Experience with website development, website builders, content management systems, or web-based technologies.
- Demonstrated ability to use AI tools to create, modify, troubleshoot, and improve websites.
- Familiarity with AI platforms, large language models (LLMs), prompt engineering, and AI-assisted development tools.
- Understanding of HTML, CSS, JavaScript, APIs, and website integrations, or the ability to work effectively with AI tools to accomplish similar tasks.
- Interest or experience in developing AI agents, automated workflows, chatbots, or client-facing AI applications.
- Strong problem-solving skills and the ability to evaluate AI-generated output, identify errors, and implement effective solutions.
- A practical, self-directed approach to learning new technologies and improving existing processes.
- Strong attention to detail, organization, and commitment to delivering high-quality work.
Preferred Qualifications
- Experience building AI agents or connecting AI systems to external tools, APIs, and business applications.
- Familiarity with automation platforms, databases, hosting environments, version control, and website analytics.
- Experience with website design principles, responsive layouts, SEO fundamentals, and user experience optimization.
- Experience improving or scaling repeatable website development processes.
What Success Looks Like
The successful candidate will consistently deliver quality websites, improve the speed and efficiency of website production, and help develop AI agents and automated workflows that reduce manual effort and enhance the client experience. Over time, this individual will help make our AI website-building capabilities more powerful, reliable, scalable, and competitive.
Pay: $60,000.00 - $85,000.00 per year
Benefits:
- 401(k) matching
- Dental insurance
- Health insurance
- Paid time off
- Referral program
- Vision insurance
Work Location: In person
Salary Context
This $60K-$85K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At HVACDirect.com, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($72K) sits 66% below the category median. Disclosed range: $60K to $85K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
HVACDirect.com AI Hiring
HVACDirect.com has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Troy, OH, US. Compensation range: $85K - $85K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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