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About This Role
The AI Platform & Security Engineer will help design, build, operate, and secure SGA Dental Partners’ AI environment. As at many companies, AI is increasingly part of SGA’s daily business operations, with hundreds of dental practices and a robust corporate team looking to quickly and securely adopt this new technology. This role is responsible for implementing guardrails and other technical controls that allow internal teams to quickly implement AI while maintaining security and compliance baselines.
The AI Platform & Security Engineer will serve as a key member of the Enterprise Architecture & Security team, working alongside other departments and business stakeholders. Priorities include establishing and centralizing hosting infrastructure, code repositories, identity provider strategy, secrets management, and source control for AI workloads. Later work will focus on automated scanning, agent evaluation, security hardening, and policy enforcement so that approved patterns can be deployed without manual review.
Pay: $125,000 - $150,000
Schedule: full time, Mon - Fri 8 - 5
Key Responsibilities
- Administer the enterprise LLM platform, including identity management, provisioning, licensing, usage monitoring, and controls.
- Build and operate the AI integration layer: MCP server and connector management, service principals, service accounts, API and secrets management, sandbox and production environments, custom and third-party integrations (Microsoft Entra, Graph, etc).
- Establish and document standard patterns for agents, so that new agents follow an approved design.
- Configure model routing across cloud providers with PHI handling requirements integrated from the beginning.
- Implement automated scanning of code and agents built by users, agent evaluation and testing, and PHI guardrails enforced in the delivery pipeline.
- Establish secure development practices for AI and internally developed software, including secrets scanning, sensitive-data detection in repositories, dependency review, and pre-commit controls.
- Implement and tune data loss prevention tools and access controls for AI traffic and AI-connected systems.
- Produce technical evidence in support of HIPAA and financial-controls requirements as part of normal system design.
- Lead technical remediation when sensitive data is found outside approved locations.
- Build, operate, and harden cloud environments that host internal applications, dashboards, and AI-powered services.
- Maintain least-privilege access across identity and access management, secrets lifecycle, source control, and deployment paths for all AI workloads.
- Build self-service infrastructure so that approved options are readily available to teams.
- Onboard users and teams onto the platform, deploy environments, and troubleshoot with users.
- Convert repeated requests into self-service capabilities and documentation through secure and compliant automation.
- Use AI coding tools (Claude Code, Codex, or equivalent) as a primary development workflow and help other engineers adopt them effectively, implementing best practices and DevSecOps principles.
- Maintain the internal code and library standards for AI workloads: shared modules, dependency management, versioning, testing, and documentation, so that code built across teams is consistent, reviewable, and supportable.
- Participate in change management and documentation for infrastructure, integrations, and controls.
- Escalate risks and control gaps to the Director of Enterprise Architecture & Security.
Knowledge/Skills/Abilities
- Daily, fluent use of AI coding tools (Claude Code, Codex, GitHub Copilot, or equivalent).
- Hands-on experience with major cloud platforms (Azure preferred) and modern identity providers (Entra ID, OAuth, service principals).
- Working knowledge of secure development practices: secrets management, static analysis, dependency scanning, and code review.
- Ability to read and reason about unfamiliar code quickly.
- Understanding of HIPAA as it applies to engineering architecture and data governance, or the ability to learn it quickly.
- Familiarity with LLM application patterns such as agents, tools / skills, retrieval, MCP connectors, and agent evaluation.
- Strong written communication; a significant part of the work is producing documentation, standards, and control evidence, along with occasional presentations or internal memos.
- Disciplined coding practices: modular design, reuse over duplication, clear naming, tests, and documentation written for the next engineer rather than the current one.
- Track and optimize AI platform spend, including seat, token, and infrastructure costs; identifying where existing tools can meet a need before AI implementation or additional spend is considered.
- Ability to work with non-technical stakeholders or executives and translate requirements into secure designs.
- Self-directed, with sound judgment about when to proceed and when to escalate.
- Comfortable handling requests from stakeholders while following proper approval policies and workflows.
- Strong ability to understand and follow corporate policy, procedures, and standards, with an emphasis on change management.
Education & Experience:
- Minimum of 4 years in software, platform, and/or security engineering with production ownership.
- Bachelor's degree in computer science, information systems, or related field / experience.
- Experience in healthcare, dental, or another regulated-data environment is preferred.
- Experience working with SQL, Postgres, or other similar relational databases is preferred.
- Security certifications (Security+, CISSP, CCSP, AAISM, SecAI+, or similar) are a plus.
- Experience with GitHub, Cloudflare, Railway, Microsoft 365, Azure, or Power BI is a plus.
- Experience in a fast-growing, multi-site organization is a plus.
- Prior IT experience is a plus.
Benefits We Offer:
- Paid Holidays and Paid Time Off (PTO)
- 401(k) Retirement Plan
- Health and Vision Insurance
- Employer-Paid Dental Insurance, Life Insurance, Care.com Membership & Employee Assistance Program
- Supplemental Coverage (Disability, Critical Illness, Accident)
- Career Growth and Development Opportunities
This position is eligible for remote work in the following states: Alabama, Arizona, California, Colorado, Florida, Georgia, Illinois, Indiana, Kansas, Kentucky, Louisiana, Maryland, Michigan, Minnesota, Mississippi, Missouri, Nevada, New Mexico, North Carolina, Ohio, Pennsylvania, South Carolina, Tennessee, Texas, Utah, Virginia, Washington, Wisconsin.
In hometown communities, SGA Dental Partners is a beacon of positive energy, delivering the highest level of clinical excellence and patient-centered care. Apply today to join us in making a difference!
In order to maintain a high level of clinical excellence, SGA Dental Partners and dental practices affiliated with SGA maintain a drug free and alcohol free workplace. All new hires are required to pass a full panel drug screening as a condition of employment.
SGA Dental Partners is an Equal Opportunity Employer and values diversity at every level of our team. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or veteran status.
Salary Context
This $125K-$150K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At SGA Dental Partners, 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 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($137K) sits 35% below the category median. Disclosed range: $125K to $150K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
SGA Dental Partners AI Hiring
SGA Dental Partners has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in GA, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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