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About This Role
Looking for a position that makes you smile?
We’re seeking an AI Developer to join our growing team.
The AI Developer is responsible for designing, building, and maintaining AI agents, copilots, and intelligent automation solutions that improve clinical, operational, and patient\-facing workflows across Smile Doctors. Working closely with the AI \& Automation Solution Architect and the Project Management Office (PMO), this position will translate solution designs into production\-ready tools by developing the code, integrations, and prompt logic that power the AI solutions used across the business each day.
How you’ll make us better:
- Build AI agents and copilots using Microsoft Copilot Studio, Azure AI Foundry, Azure OpenAI, and other LLM\-based platforms
- Develop and tune prompts, RAG pipelines, knowledge grounding, and tool/function integrations for production AI assistants
- Implement automation flows in Power Automate to support AI\-enabled workflows
- Write clean, maintainable code in Python, C\# or PowerShell for data processing, orchestration, and AI logic
- Integrate AI solutions with internal systems including EHR/PMS platforms, Microsoft 365, SharePoint, Dataverse, and internal APIs
- Build and maintain reusable components, connectors, and integration patterns
- Develop unit tests, evaluation harnesses, and quality checks for model and prompt performance
- Deploy solutions through approved CI/CD and release processes
- Monitor running agents and automations; troubleshoot accuracy, latency, and cost issues
- Tune prompts, retrieval, and model selection to improve quality and reduce token spend
- Maintain documentation for solutions, prompts, data sources, and operating runbooks
- Partner with the AI \& Automation Solution Architect, PMO, and business stakeholders to translate requirements into working solutions
- Follow established standards for development, testing, security, and deployment
- Apply responsible AI practices, including PII/PHI handling, content filtering, human\-in\-the\-loop where appropriate, and audit logging
- Contribute to AI literacy by sharing patterns, demos, and reusable assets with the broader IT team
- This role requires use of approved AI tools to perform assigned tasks more efficiently and effectively
Your special skills:
- Strong problem solving skills
- Ability to work independently and across business and technical teams
Prerequisites for success:
- Bachelor’s degree in Computer Science, Engineering, or a related field required
- Three (3\) years of software development experience required, healthcare or other regulated industries preferred
- One (1\) year of experience building AI/LLM\-based applications (agents, copilots, RAG, or fine\-tuning) required
- Experience deploying production solutions on Azure or comparable cloud required
- Working knowledge of Python and/or C\#
- Working knowledge of Microsoft Copilot Studio, Azure AI Foundry, Azure OpenAI, or comparable LLM platforms such as OpenAI, Anthropic, or Google
- Working knowledge of prompt engineering, including grounding techniques, RAG pipelines, and vector stores
- Working knowledge of Power Platform (Power Automate, Power Apps, Dataverse) and/or RPA tools such as UiPath, Automation Anywhere, or Blue Prism
- Familiarity with TypeScript or JavaScript
- Familiarity with DevOps practices including Git, CI/CD, environments, and automated testing
- Solid understanding of APIs, microservices, authentication (OAuth/Entra ID), and integration frameworks
- Solid understanding of data privacy, security, and responsible AI principles, with the ability to apply them to HIPAA\-compliant processes and solutions
We saved the best for last.
In exchange for the dynamic contribution you’ll bring to our team, we offer:
- Competitive salary
- Medical, dental, vision and life insurance
- Short and long\-term disability coverage
- 401(k) plan
- 3 weeks paid time off in your first year \+ paid holidays
- Discounts on braces and clear aligners for you and your family members
Why Smile Doctors?
As the nation’s leading Orthodontic Support Organization, Smile Doctors is shaping the future of orthodontics through strategic partnerships with top local doctors. We provide best\-in\-class support services so our partner orthodontists can focus on what they do best — driving extraordinary treatment outcomes and providing patients with an unmatched experience.
With hundreds of partnered practices across the nation, our synergistic approach has made us the fastest\-growing organization in our industry and produced an ever\-expanding need for top talent as we continue our unprecedented trajectory. To us, there’s no such thing as “top of our game.” We’re always climbing higher — together. And as our business grows, there’s plenty of room for our team to grow their careers, too.
Our dynamic support services team is comprised of world\-class professionals whose diverse experiences drive innovation and development. Together, we are committed to passionately helping others achieve their best, most confident smiles.
IND123
*This is the perfect opportunity to grow with an expanding organization! Apply today!*
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Smile Doctors, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Smile Doctors AI Hiring
Smile Doctors has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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