AI Systems Engineer

AI & Systems Engineer Position at WebMD

WebMD is the most recognized and trusted brand of health information and the leading provider of health information services, serving consumers, physicians, healthcare professionals, employers and health plans through our public and private online portals and WebMD the Magazine. The WebMD Health Network includes WebMD, Medscape, MedicineNet, eMedicine, RxList, theheart.org and Medscape Education. Our consumer portals and mobile health applications provide engaging, relevant and credible health and wellness information, personalized health assessment tools and access to online communities. WebMD is an Equal Opportunity/Affirmative Action employer and does not discriminate on the basis of race, ancestry, color, religion, sex, gender, age, marital status, sexual orientation, gender identity, national origin, medical condition, disability, veterans status, or any other basis protected by law.

The AI & Systems Engineer is a hybrid infrastructure and AI operations role responsible for managing the full enterprise IT stack while also architecting, deploying, securing, and governing AI systems across the organization. This role serves as the primary technical owner for AI platform integrations—including large language models, MCP servers, and connector ecosystems—alongside traditional systems administration responsibilities covering Windows Server, cloud platforms, network infrastructure, and enterprise security. The ideal candidate bridges deep infrastructure expertise with hands-on AI engineering, ensuring AI systems are deployed with rigor, properly hardened, and tightly integrated with existing enterprise identity and security controls.

Position Requirements

Infrastructure & Systems Administration

• Experience across the complete infrastructure stack: network, security, storage, hardware, and OS layer
• Expertise with Windows Server administration, configuration, upgrades, and lifecycle management
• Deep expertise in PowerShell scripting for automation, provisioning, reporting, and systems management
• Expertise in DNS and DHCP administration, including Windows Server DNS roles, zone management, conditional forwarding, split-brain DNS, and DNSSEC
• Experience with enterprise backup tools including NetApp; building DR environments and failover plans
• Proficiency with virtualization platforms: VMware and/or Hyper-V
• Experience managing and maintaining security patches across server and application estates
• Experience migrating data across cloud, hybrid, and on-premises environments
• Ability to plan, organize, and document complex system maintenance activities; configure systems consistent with institutional policies and procedures
• Comfortable with on-call schedules and response to critical alerts in a timely manner

Cloud & Productivity Platforms

• Experience with Google Workspace administration (user lifecycle, OU management, GAM scripting)
• Experience with Google Cloud Platform (GCP) infrastructure and services
• Experience with Microsoft Office 365 administration including licensing, Exchange Online, and compliance
• Experience with Microsoft Azure including Entra ID (formerly Azure AD), Conditional Access, PIM, and Azure resource management
• Familiarity with setting up and configuring applications with Azure SSO or Google SSO (SAML, OIDC, OAuth 2.0)

AI Systems Engineering & Operations

• Hands-on experience deploying, configuring, and administering large language model (LLM) platforms including Anthropic Claude (claude.ai, Claude API, Claude Code) and Google Gemini across enterprise environments
• Experience architecting and administering MCP (Model Context Protocol) server infrastructure: deploying MCP server instances, configuring tool registries, managing connector ecosystems (Airtable, Atlassian, Google Drive, Gmail, and others), and integrating MCP servers with enterprise identity and access controls
• Ability to design and enforce AI connector governance policies: scope management, permission auditing, credential lifecycle, and connector access reviews
• Experience performing AI platform security assessments covering permission scopes, data flows, output validation pipelines, prompt injection defenses, and HITL (Human-in-the-Loop) controls
• Familiarity with AI hardening principles: model access controls, rate limiting, WAF integration, API gateway configuration, session controls, and kill switch hierarchies for AI systems
• Experience configuring and monitoring SIEM detection rules for AI platform activity and anomaly detection
• Understanding of responsible AI deployment including data residency requirements, privacy-by-design, audit logging, and regulatory alignment (HIPAA, GDPR as applicable)
• Ability to evaluate new AI tools and platforms against enterprise security standards prior to production deployment
• Experience authoring AI systems documentation: architecture diagrams, runbooks, security assessments, and governance policies

Security & Compliance

• Knowledge of applicable data privacy practices, laws, and regulations (HIPAA, SOC 2, GDPR fundamentals)
• Experience with identity and access management (IAM) tooling: Entra ID, Active Directory, SAML/OIDC federation, MFA enforcement, and privileged access management
• Familiarity with network security concepts including firewall policy, VPN administration, WAF configuration, and zero-trust network access models
• Experience with endpoint management using Microsoft Intune or comparable MDM solutions

Certifications & Nice-to-Have

• Certifications in VMware, Hyper-V, MCSE, MCSA, or AWS/Azure Solutions Architect are desirable
• Certifications or coursework in AI/ML platforms, prompt engineering, or responsible AI are a plus
• Experience with infrastructure-as-code tooling (Terraform, Bicep, or comparable) is advantageous
• Familiarity with SIEM platforms (Microsoft Sentinel, Splunk, or similar) for security monitoring
• Experience with acquisition IT integration: user provisioning, mailbox migrations, SSO federation, and directory consolidation

Roles & Responsibilities

Infrastructure Operations

• Provide technical support to corporate business units and support their applications across the enterprise
• Maintain the back-end IT infrastructure estate including servers, storage, UPS, and Hyper-V environments
• Perform Windows Server upgrades, rebuilds, and OS lifecycle management
• Manage and maintain security patches across server infrastructure on a defined cadence
• Migrate data across cloud, hybrid, and on-premises environments with documented rollback plans
• Administer and maintain Windows Server DNS including zone health, record lifecycle, replication, and conditional forwarding configurations
• Build and maintain DR environments, runbooks, and failover/failback plans; participate in DR testing exercises
• Work with or without formal SOPs; author and maintain runbooks and technical documentation as environments evolve
• Proactively learn and document the environments of current and future acquired companies to enable smooth integration
• Serve as a thought leader and architect new IDF/MDF configurations; identify opportunities to scale back or synergize infrastructure across the portfolio
• Be comfortable with on-call schedules and respond to critical alerts in a timely and effective manner

AI Systems Engineering & Governance

• Own the deployment, configuration, and ongoing administration of enterprise AI platforms including Anthropic Claude (claude.ai, Claude API, Claude Code) and Google Gemini; manage platform accounts, access policies, and usage governance
• Architect and manage the enterprise MCP server infrastructure: deploy and maintain MCP server instances, configure tool and connector registries, manage connector lifecycle (onboarding, auditing, deprecation), and enforce data flow and permission policies across connectors
• Integrate AI platforms and MCP servers with enterprise IAM controls (Entra ID / Azure AD SSO, Conditional Access, MFA) to ensure AI system access is governed through standard identity pipelines
• Conduct periodic AI connector and platform security assessments covering permission scopes, credential exposure, data residency, output validation, and prompt injection risk; document findings and drive remediation
• Implement and maintain AI hardening controls: API gateway rate limiting, WAF rules for AI endpoints, session management, kill switch procedures, and anomaly monitoring via SIEM
• Partner with business units to evaluate new AI tools and vendor platforms; perform pre-deployment security reviews and produce architecture documentation before production rollout
• Maintain comprehensive documentation for all AI systems: architecture diagrams, security assessment reports, connector governance policies, and operational runbooks
• Stay current with developments in enterprise AI platforms, MCP ecosystem tooling, and AI security best practices; translate emerging capabilities into actionable infrastructure improvements