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About This Role
COMPANY SUMMARY:
To achieve and maintain our high-quality standards, we seek to partner with physicians and staff who share our vision, values and brand promises and are dedicated to advancing our culture. QualDerm providers and staff are well-trained, highly skilled and recognized as leaders in the specialty of dermatology.
We currently have 158 practices in 17 states, spanning across the full spectrum of dermatology, skin cancer care, cosmetics, plastic surgery, and pathology with continued plans to expand further across the nation.
We will partner with you to establish the best combination of environment, technology, teamwork and personal reward opportunities to earn your confidence that we are your best place to work.
PURPOSE:
The AI, Data & Automation Engineer builds and evolves the organization's modern data platform and the intelligent automations that run on top of it. This role combines data engineering (Snowflake pipelines and models), analytics and visualization (Power BI), and process automation (LLM-powered workflows, RPA, and low-code tools) to turn internal and external data into trusted KPIs, actionable insights, and measurable operational efficiency.
The position is responsible for both the technical foundation of the data platform and the delivery of analytics and automation solutions that business teams rely on every day.
ESSENTIAL DUTIES AND RESPONSIBILITIES:
Data Platform & Engineering
- Design, build, and optimize scalable ELT/ETL pipelines in Snowflake, ingesting data from diverse internal systems, third-party sources, and APIs while ensuring data quality, integrity, and performance.
- Develop and maintain dimensional and analytical data models, using tools such as SQL, dbt, Snowpark, Streams/Tasks, and orchestration frameworks.
- Monitor pipeline health and manage cost, performance tuning, and access controls (roles, warehouses, resource usage) within Snowflake.
- Apply version control, testing, and CI/CD practices to data and automation assets.
Analytics & Visualization
- Build analytics solutions that answer critical business questions, inform decisions, and drive operational efficiency.
- Develop, publish, and maintain Power BI reports, dashboards, and semantic models (DAX, Power Query, row-level security) that communicate insights clearly to audiences ranging from frontline teams to executives.
- Support development and tracking of KPIs and business metrics, and support ad hoc analysis projects as needed.
AI & Automation
- Design and deploy LLM-powered solutions such as document and text extraction, summarization, classification, intelligent search/RAG over enterprise data, and AI-assisted reporting, using Snowflake Cortex, Azure OpenAI, or comparable platforms.
- Develop prompts, evaluation methods, and guardrails to ensure AI outputs are accurate, auditable, and appropriate for business use.
- Build and maintain RPA and workflow automations (e.g., Power Automate, UiPath, or similar) that eliminate manual, repetitive processes and integrate with the data platform and business systems.
- Identify and prioritize opportunities to increase efficiency through automation; document expected value and measure results after deployment.
Collaboration & Governance
- Partner with cross-functional teams and business stakeholders to gather and refine requirements and translate them into data, analytics, and automation deliverables.
- Develop and foster relationships with internal and external partners to manage work-streams and cross-functional deliverables.
- Partner with senior leaders to support data governance, security, and responsible AI practices, including protection of sensitive data (e.g., PHI/PII) and compliance with applicable regulations.
- Build stakeholder trust in data accuracy and cleanliness through documentation, validation, and clear communication.
- Learn the business quickly and stay current on emerging data, AI, and automation technologies.
- Comply with organizational policies and procedures as outlined in the Teammate Handbook and Policy & Procedure Manuals.
Requirements
Required
- Bachelor's degree in a related field (e.g., Computer Science, Information Systems, Data Science, Engineering, Analytics) or equivalent experience.
- 2-4 years of experience as a data engineer, analytics engineer, data analyst, or automation developer.
- Hands-on experience with Snowflake, including advanced SQL and building and optimizing data pipelines and models.
- Hands-on experience with Power BI, including data modeling, DAX, and dashboard design.
- Experience with Python or another scripting language for data processing and automation.
- Experience in data management disciplines including data integration, modeling, optimization, and data quality.
- Experience working in cross-functional teams and collaborating with business stakeholders on data and analytics initiatives.
- Healthcare industry experience.
Preferred
- Experience building solutions with LLMs and generative AI (prompt engineering, RAG, API integration, evaluation), e.g., Snowflake Cortex, Azure OpenAI, or similar.
- Experience with RPA and workflow automation tools (e.g., Power Automate, UiPath, Automation Anywhere).
- Familiarity with the Azure ecosystem (Azure Data Factory, Synapse, Azure Functions) and with dbt or comparable transformation/orchestration tools.
- Experience with Git, CI/CD, and DevOps practices for data and automation.
- Knowledge of data governance, security, and privacy practices, including HIPAA.
- Snowflake, or cloud/AI certifications.
Benefits Benefits of joining Qualderm Partners:
- Competitive Pay – Attractive compensation to reward your hard work
- Comprehensive Health Coverage – Includes Medical, Dental, and Vision plans to keep you covered
- Generous 401(k) Plan – Company matches 100% of the first 3%, plus 50% of the next 2%, with immediate vesting
- Paid Time Off (PTO) – Accrue PTO from day one, plus enjoy 6 paid holidays and 2 floating holidays each year
- Company-Paid Life Insurance – Peace of mind with basic life coverage, with the option for additional plans
- Disability Protection – Short-term and long-term disability coverage to protect you in unexpected circumstances
- Additional Wellness Plans – Accident, critical illness, and identity theft protection plans for extra security
- Employee Assistance Program (EAP) – Access confidential support for personal or work-related challenges
- Exclusive Employee Discounts – Save on products and services with special discounts just for you
- Referral Bonus Program – Earn bonuses by referring qualified candidates to join the team
QualDerm Partners is proud to be an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. Applicants must be currently authorized to work in the United States in a full-time basis.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At QualDerm Partners LLC, 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 $211,950 based on 13,048 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 $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
QualDerm Partners LLC AI Hiring
QualDerm Partners LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Brentwood, TN, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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