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Model Server Jobs in Pennsylvania (NOW HIRING)

WebMD

AI Systems Engineer

Yardley, PA · Hybrid

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 ...

Tria

Server/Bartender/Wine Lover

Philadelphia, PA · On-site

$20/hr

We aim to become a model for other restaurants by taking a leadership role in reshaping how our ... Here is a sampling: * $20 minimum wage per pay period for servers and bartenders ($15 for hosts and ...

WebMD

AI Systems Engineer

Yardley, PA · On-site

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 ...

Compunnel

GRC Archer Developer

Pittsburgh, PA · On-site

... server-side maintenance. Key Responsibilities • Implement and configure RSA Archer solutions ... Develop data models, business process models, and solution prototypes based on business ...

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Model Server information

See Pennsylvania salary details

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How much do model server jobs pay per hour?

As of Sep 8, 2026, the average hourly pay for model server in Pennsylvania is $15.33, according to ZipRecruiter salary data. Most workers in this role earn between $10.38 and $17.36 per hour, depending on experience, location, and employer.

What is a model server?

Model servers are specialized software systems designed to deploy, manage, and serve machine learning models in production environments. They allow developers and organizations to expose trained models via APIs, enabling real-time predictions or batch processing for various applications. Model servers handle incoming requests, manage model versions, and ensure scalability, reliability, and security when serving models. Common examples include TensorFlow Serving, TorchServe, and NVIDIA Triton Inference Server.

How does a model server typically collaborate with data scientists and engineers during model deployment?

Model Servers play a key role in bridging the gap between data science teams and engineering teams during model deployment. They are responsible for integrating trained machine learning models into production environments, ensuring scalability, reliability, and low-latency access. Collaboration often involves regular communication with data scientists to understand model requirements and with engineers to align on infrastructure, security, and monitoring needs. This teamwork helps ensure that deployed models perform well and can be efficiently maintained or updated as new versions are released.

What are the key skills and qualifications needed to thrive as a model server, and why are they important?

To thrive as a Model Server engineer, you need expertise in deploying, managing, and optimizing machine learning models, typically supported by a background in computer science or a related field. Familiarity with tools like TensorFlow Serving, TorchServe, Docker, Kubernetes, and cloud platforms such as AWS or GCP is essential, along with knowledge of REST or gRPC APIs. Strong problem-solving, communication, and collaboration skills help in troubleshooting issues and working with data scientists and DevOps teams. These abilities ensure reliable, scalable, and efficient delivery of machine learning services in production environments.

What is the difference between Model Server vs Data Scientist?

AspectModel ServerData Scientist
Required CredentialsTypically requires knowledge of machine learning deployment, programming, and sometimes certifications in cloud platformsRequires degrees in data science, statistics, or related fields; certifications are optional
Work EnvironmentWorks primarily in IT, cloud environments, or data infrastructure teamsWorks in research, analytics, or business teams, often in office settings
Employer & Industry UsageUsed in tech companies, AI firms, and organizations deploying machine learning modelsEmployed across industries like finance, healthcare, marketing, and tech for data analysis

The main difference is that Model Servers focus on deploying and maintaining machine learning models in production environments, while Data Scientists analyze data, develop models, and generate insights. Model Servers are essential for operationalizing models, whereas Data Scientists drive the creation and refinement of those models.

What are the most commonly searched types of Model Server jobs in Pennsylvania?

The most popular types of Model Server jobs in Pennsylvania are:

What are popular job titles related to Model Server jobs in Pennsylvania?

For Model Server jobs in Pennsylvania, the most frequently searched job titles are:

What job categories do people searching Model Server jobs in Pennsylvania look for?

The top searched job categories for Model Server jobs in Pennsylvania are:

Model Server jobs near you
Infographic showing various Model Server job openings in Pennsylvania as of August 2026, with employment types broken down into 81% Full Time, and 19% Part Time. Highlights an 100% In-person job distribution, with an average salary of $31,884 per year, or $15.3 per hour.

AI Systems Engineer

Yardley, PA • Hybrid

WebMD
1 - 5K employees
Learn more about WebMDexternal

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Posted 29 days ago

Job description

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.

Position Overview

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 & ResponsibilitiesInfrastructure 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
  • Collaborate with legal, compliance, and security teams to ensure AI platform usage aligns with HIPAA, data privacy obligations, and organizational policy
Collaboration & Stakeholder Engagement
  • Establish strong working relationships with corporate business units, the Operations team, IT leadership, and direct manager
  • Work cross-functionally to coordinate large-scale infrastructure efforts, migrations, and AI platform rollouts
  • Represent IT infrastructure and AI operations as a subject-matter expert in cross-team planning and architecture discussions
  • Communicate project status, risks, and decisions clearly to both technical and non-technical stakeholders

Salary range: $120,000 - $145,000

Bonus Eligible:This position is also eligible for a discretionary company bonus, based upon business results.

Benefits: Employees in this position are eligible to participate in the company sponsored benefit programs, including the following within the first 12 months of employment:

  • Health Insurance (medical, dental, and vision coverage)
  • Paid Time Off (including vacation, sick leave, and flexible holiday days)
  • 401(k) Retirement Plan with employer matching
  • Life and Disability Insurance
  • Employee Assistance Program (EAP)
  • Commuter and/or Transit Benefits (if applicable)

Eligibility for specific benefits may vary based on job classification, schedule (e.g., full-time vs. part-time), work location and length of employment.

Employment Type: Full-Time

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