Please find the below details :

Role : Consultant – AI Application Support (Level 2)
Experience : 4-6 years
Location : Bangalore & Gurgaon

Role purpose : 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.

Key responsibilities (what you will do)
1) L2 Ticket Handling, Triage & Ownership (ServiceNow)
Pick up escalations from L1 Service Desk and take ownership through resolution or appropriate escalation.
Validate and confirm impact, urgency, and priority , ensuring correct categorization and assignment .
Perform structured triage to classify issues as:
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)
Create and maintain Support Manuals , Runbooks , SOPs , and Troubleshooting Guides for each supported AI application.
Write and curate 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
Identify monitoring gaps and recommend improvements (alerts/dashboards/logging standards) to reduce MTTR.
Support go-live readiness for recent AI apps by ensuring:
support model and escalation paths are defined
runbooks and known dependencies are documented
logging/telemetry is sufficient for L2 diagnosis
common failure modes and mitigations are captured
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)
Working knowledge of AI platforms : OpenAI / Azure AI, common operational failure patterns (auth, throttling, safety filters,



latency, deployment errors).
Strong cloud fundamentals : Azure (preferred) plus familiarity with AWS and GCP (networking basics, IAM, logging/monitoring, secrets management, API services).
Strong 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.
Excellent 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

📌 AI Application Support (Bengaluru)
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