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About This Role
Company Description
Integrated Dermatology is a leading national dermatology practice that acquires and partners with dermatology practices across the United States. The culture at ID is a reflection of its people. Our environment is filled with hard-working, dynamic individuals who come together to ensure the success of our partner dermatologists.
Position Summary:
The Business Applications & AI Engineer is responsible for the strategy, design, customization, integration, administration, and continuous improvement of Integrated Dermatology's business application ecosystem. This position serves as the primary technical resource for Dynamics 365, Power Platform, HubSpot, Microsoft Copilot, AI solutions, workflow automation, and application integration initiatives across the organization.
The Business Applications & AI Engineer works closely with business stakeholders, operational leadership, and technical teams to identify opportunities for process improvement, automate manual tasks, improve data quality, and deliver scalable business solutions. This role is responsible for gathering requirements, documenting business processes, developing technical solutions, managing application enhancements, and ensuring solutions align with organizational goals and governance standards.
The ideal candidate combines strong technical development skills with business analysis capabilities and can effectively translate business requirements into practical, maintainable technology solutions.
Job Description
- Serve as the primary technical owner and subject matter expert for business applications including Dynamics 365, Power Apps, Dataverse, HubSpot, Microsoft Copilot, and other enterprise business systems.
- Design, develop, customize, configure, and maintain business applications, workflows, forms, automations, integrations, and data models to support business objectives.
- Conduct stakeholder interviews, workshops, and requirement-gathering sessions to identify business needs and document detailed functional and technical requirements.
- Create business requirements documents, solution designs, scope documents, process flows, user stories, acceptance criteria, and implementation plans.
- Design, develop, and maintain integrations between enterprise applications utilizing APIs, Power Automate, Azure Integration Services, Microsoft Graph, web services, and other approved integration platforms.
- Develop and support AI-enabled solutions leveraging Microsoft Copilot, Copilot Studio, Power Platform AI capabilities, Azure AI services, and approved generative AI technologies.
- Participate in Agile development processes including sprint planning, backlog refinement, effort estimation, sprint reviews, retrospectives, and release management.
- Collaborate with business leaders, department stakeholders, vendors, and technical teams to ensure successful delivery of business application solutions.
- Develop and maintain operational and executive reporting solutions using Microsoft Power BI, including data preparation, semantic models, dashboards, reports, visualizations, and appropriate data-access controls.
- Create and maintain application documentation, training materials, governance standards, support documentation, and knowledge transfer materials.
- Analyze existing business processes and identify opportunities for workflow optimization, automation, system consolidation, and data standardization.
- Establish and maintain application governance standards, security models, change control processes, development standards, testing procedures, and lifecycle management practices for business applications.
- Coordinate with software vendors, implementation partners, consultants, and internal stakeholders to troubleshoot issues, manage application enhancements, and evaluate new platform capabilities.
- Develop and deliver user training, documentation, adoption strategies, and change-management activities for new business application solutions and AI initiatives.
- Evaluate emerging AI technologies and identify opportunities to leverage Microsoft Copilot, Copilot Studio, Azure AI services, and automation technologies to improve business outcomes and operational efficiency.
Qualifications
- Advanced experience administering and customizing Microsoft Power Platform, including Power Apps, Power Automate, and Dataverse, with additional experience in Dynamics 365, HubSpot, or comparable business application platforms.
- Experience designing, developing, and supporting integrations using APIs, Power Automate, Microsoft Graph, REST services, webhooks, and related integration technologies.
- Experience conducting requirements gathering, business process analysis, stakeholder interviews, and solution design activities.
- Experience working in Agile, Scrum, Kanban, or sprint-based application development environments.
- Experience implementing and supporting Microsoft Copilot, Copilot Studio, AI-assisted workflows, automation solutions, or comparable AI technologies.
- Strong understanding of relational databases, data modeling, application security, business process design, and platform governance.
- Moderate hands-on experience with Microsoft Power BI, including Power Query, data modeling, report and dashboard development, basic DAX, workspace management, scheduled refresh, and report security.
- Strong communication, documentation, presentation, facilitation, and stakeholder-management skills.
- Experience customizing CRM, ERP, workflow, business intelligence, or operational platforms within multi-location organizations.
- Experience creating technical specifications, solution architecture documentation, user stories, process maps, and scope-of-work documentation.
Preferred Qualifications:
- Databricks experience.
- Power Platform Developer certification.
- Dynamics 365 certification.
- HubSpot Sales Hub or Marketing Hub certification.
- Microsoft Copilot certification.
- Azure AI experience.
- Business systems experience within healthcare organizations.
Additional Information Job Type:
- Full-time
- #LI-HYBRID
Experience:
- 5+ years designing, customizing, developing, integrating, and supporting enterprise business applications, workflow automation solutions, and business process platforms.
Education:
- Bachelor's degree in Information Systems, Computer Science, Engineering, Business Technology, or related field preferred. Equivalent professional experience may be considered.
Certifications/Licenses:
- Microsoft Power Platform Developer Associate, Dynamics 365 certification, Microsoft Copilot certification, Azure AI certification, or comparable application-development certifications preferred.
Integrated Dermatology and it's entities 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Integrated Dermatology, 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.
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.
Integrated Dermatology AI Hiring
Integrated Dermatology has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boca Raton, FL, US.
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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