Support Engineer Jobs

About The Position

Requirements

• Relevant experience in production support, application support, or platform operations, with the ability to investigate routine incidents independently and recognise when specialist escalation is required.
• Practical Linux troubleshooting skills and familiarity with Windows-based developer environments.
• Working proficiency in Python, Bash, and/or PowerShell for operational checks, log analysis, and straightforward support automation.
• Experience using SQL to investigate application issues and query operational records, ideally in PostgreSQL.
• Practical experience interpreting metrics, logs, traces, and dashboards to distinguish a platform-wide incident from a user-, client-, model-, or region-specific issue.
• Confidence troubleshooting API-driven services, including HTTP errors, authentication, networking, timeouts, retries, rate limiting, and streaming connections.
• Hands-on experience supporting LLM-based platforms or services, with a working understanding of LLM APIs, including prompts, context limits, token usage, model identifiers, and streaming responses.
• Familiarity with public-cloud services, ideally AWS, and the ability to inspect containerised workloads using established tools and procedures.
• Clear communication with technical and non-technical users, including concise incident updates, realistic expectations, and well-documented escalations.
• Awareness of access controls, sensitive-data handling, and safe diagnostic practices, including avoiding exposure of prompts, credentials, or confidential content in support records.
• A methodical approach to troubleshooting, prioritisation, and cross-region handovers.

Nice To Haves

• Experience supporting AWS Bedrock or other managed model APIs.
• Experience supporting AI coding assistants, API clients, or developer workspaces such as Claude Code, Codex CLI, OpenCode, PI, or Coder.
• Familiarity with Grafana, Coralogix, or equivalent monitoring and log-analysis tools.
• Exposure to LLM gateways or proxies such as LiteLLM, or internally hosted inference services such as vLLM.
• Experience troubleshooting containerised services on ECS or Kubernetes.
• Familiarity with identity and access provisioning, particularly Okta, SCIM, and application-level permissions.
• Experience supporting multi-region services and working with globally distributed support and engineering teams.
• Experience coordinating support cases with cloud providers or software vendors during service degradation.

Responsibilities

• Provide first- and second-line support for QRT’s LLM platform, investigate issues raised by engineering and business users, maintain support tickets, and follow issues through to resolution or engineering escalation.
• Monitor platform health using existing dashboards, logs, and alerts; identify availability, latency, and error-rate problems; and perform approved recovery actions using established runbooks.
• Troubleshoot gateway and model-provider issues, including authentication failures, rate limits, timeouts, connection resets, streaming failures, and regional performance differences.
• Determine whether incidents originate in a user’s client or configuration, the platform gateway, an internally hosted model, or an external LLM provider; collect diagnostic evidence and coordinate escalation to the appropriate team or vendor.
• Support coding assistants, API clients, and developer workspaces connected to QRT’s LLM platform, including Claude Code, Codex CLI, OpenCode, PI, and Coder; investigate compatibility issues following client or vendor updates.
• Help users configure supported integrations, select the appropriate regional endpoint, and migrate API access from Open WebUI to the direct gateway.
• Investigate access and model-availability queries, check user permissions, and escalate provisioning or entitlement issues through established support and governance processes.
• Carry out operational checks for releases and configuration changes, including non-production validation, post-release smoke testing, and approved rollback procedures, in coordination with platform engineering.
• Use existing observability tools to investigate time to first token, time per output token, percentile latency, and failure rates; flag recurring issues and monitoring gaps to the platform team.
• Maintain runbooks, troubleshooting guides, and known-issue records; provide clear incident updates and reliable handovers between regional support teams.
• Maintain small support scripts and diagnostic utilities to reduce repetitive checks and improve evidence collection.

Benefits

• Competitive benefits package
• Discretionary performance-based bonuses