AI Platform & Security Engineer

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, r…