Please find the below details : Role : Consultant – AI Application Support (Level 2)Experience : 4-6 yearsLocation : Bangalore & GurgaonRole 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), orTechnical 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 StackTroubleshoot 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 behaviourtimestamps, request IDs/correlation IDslogs/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 hypothesissanitized 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 ReadinessIdentify monitoring gaps and recommend improvements (alerts/dashboards/logging standards) to reduce MTTR.Support go-live readiness for new AI apps by ensuring: support model and escalation paths are definedrunbooks and known dependencies are documentedlogging/telemetry is sufficient for L2 diagnosiscommon failure modes and mitigations are captured6) Problem Management & Continuous ImprovementDetect 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).Robust 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-haveFamiliarity 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 escalationMTTR for L2-resolvable incidentsSLA compliance (response/resolution) for assigned queue/categoriesFirst-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 inputsInterfaces & working relationshipsL1 Service Desk: coaching via KB/runbooks; quality feedback on triage and ticket captureL3 Product Support (Advisory UK): escalation partner for deep product defects and complex technical issuesCloud/Platform teams: support for platform incidents, capacity/limits, identity/networking issuesApp Owners / SMEs: functional clarifications, change/release coordination, operational readiness

📌 Ai Application Support (Bengaluru)
🏢 Promaynov Advisory Services
📍 Bengaluru