AI Systems Engineer

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
(
T
erraform,
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
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.