Role title
Consultant – AI Application Support (Level 2)
Role purpose (why the job exists)
Provide advanced
Level 2 (L2)
support for AI-based applications by performing deep triage, troubleshooting, and resolution of incidents and service requests escalated from
L1 Service Desk
. Distinguish
functional queries
from
technical incidents
, restore service quickly, and escalate to
L3 Product Support (Advisory UK service lines)
with complete diagnostic context. Strengthen operational readiness for rapid scale (20 apps → 100+ apps) through
knowledge management
,
documentation
, and
observability/reliability
improvements.
SFIA 8 accountability level
SFIA Level 4 – Enable
- Works under general direction within clear frameworks and processes
- Resolves complex issues and delivers outcomes independently for assigned services/applications
- Contributes to continuous improvement, operational readiness, and knowledge maturity
- Influences L1 effectiveness through guidance, documentation, and deflection content
Key responsibilities (what you will do)
1) L2 Ticket Handling, Triage & Ownership (ServiceNow)
L1 Service Desk
and take ownership through resolution or appropriate escalation.
impact, urgency, and priority
, ensuring correct
categorization
and
assignment
.
Functional queries
(usage/how-to, expected behaviour, configuration guidance), or
Technical incidents
(errors, outages, latency, integration failures, platform degradation).
- Apply runbooks/diagnostic playbooks to resolve issues within L2 scope and document all actions in ServiceNow.
2) Technical Troubleshooting Across AI + Cloud Stack
Troubleshoot failures spanning:
- AI platforms: OpenAI / Azure AI patterns such as authentication errors, throttling (rate limits), token/context issues, deployment/model availability, safety/content filtering impacts, latency/timeouts.
Cloud services
(Azure preferred; AWS/GCP familiarity): identity/IAM, networking, certificates, secrets/key vaults, API gateways, resource limits, configuration drift.
- Application dependencies: integrations, data sources, retrieval pipelines (if applicable), middleware, and downstream service availability.
3) High-Quality Escalation to L3 (Advisory UK Product Support)
When L2 resolution is not possible, escalate to L3 with a complete evidence package to minimize back-and-forth:
- reproduction steps and expected vs actual behaviour
- timestamps, request IDs/correlation IDs
- logs/metrics/traces (e.g., App Insights/Azure Monitor where applicable)
- environment details (prod/non-prod, region, model/deployment name, endpoint)
- recent changes/releases, interim mitigations applied, and L2 hypothesis
- sanitized payload samples (compliant with data handling rules)
4) Knowledge Management & Documentation (Deflection + Speed-to-Resolve)
Support Manuals
,
Runbooks
,
SOPs
, and
Troubleshooting Guides
for each supported AI application.
Knowledge Articles
in ServiceNow to enable L1 deflection and consistent resolution.
- Maintain “Known Errors / Known Issues” records and update them based on recurring patterns and fixes.
5) Observability, Reliability & Operational Readiness
6) Problem Management & Continuous Improvement
- Detect trends from incident patterns (e.g., repeated throttling, recurring auth failures, dependency outages).
- Raise and contribute to
problem records
with evidence and recommended preventive actions.
- Participate in post-incident reviews and implement corrective/preventive tasks within the support scope.
Skills required (must-have)
- AI platforms: OpenAI / Azure AI, common operational failure patterns (auth, throttling, safety filters, latency, deployment errors).
Azure
(preferred) plus familiarity with
AWS and GCP
(networking basics, IAM, logging/monitoring, secrets management, API services).
problem-solving and triage
skills; ability to isolate whether issue is app defect, platform issue, integration issue, or user/process issue.
ServiceNow
(or equivalent ITSM) proficiency: incident/request/problem workflows, categorization, SLAs, knowledge base.
documentation
skills: clear, structured runbooks and KB articles.
- Strong written and verbal communication; comfortable collaborating with L1, app owners, platform teams, and L3 engineering/product support.
Experience (typical)
- 4+ years in production support / operations (L2/L3-facing), ideally in cloud-native environments.
- Exposure to AI/ML or AI-enabled applications in production is strongly preferred.
Desirable / nice-to-have
- Familiarity with observability tooling and queries (e.g., KQL/App Insights, Azure Monitor; equivalents in AWS/GCP).
- Basic scripting/automation (PowerShell/Python) to accelerate diagnostics and reduce repeat toil.
- ITIL Foundation or equivalent service management training.
Key measures of success (KPIs)
Time-to-triage
from L1 escalation
MTTR
for L2-resolvable incidents
SLA compliance
(response/resolution) for assigned queue/categories
First-time-right escalation quality
(L3 acceptance rate; reduced ping-pong)
Knowledge contribution rate
(KAs/runbooks created/updated; KB reuse/deflection)
Recurring incident reduction
through problem management inputs
Interfaces & working relationships
- L1 Service Desk: coaching via KB/runbooks; quality feedback on triage and ticket capture
- L3 Product Support (Advisory UK): escalation partner for deep product defects and complex technical issues
- Cloud/Platform teams: support for platform incidents, capacity/limits, identity/networking issues
- App Owners / SMEs: functional clarifications, change/release coordination, operational readiness
