KPMG—Watts Application Support Engineer – Consultant – MFT – KGS CH

Job description

Description: 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 Requirements: 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 Responsibilities: 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 new 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.