Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology and trading expertise has shaped QRT’s collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.

You will provide day-to-day production support for QRT’s platform for accessing internally hosted and external LLMs. Based in the US, you will support users during US business hours and coordinate with colleagues in other regions to ensure effective incident handovers.

This is a hands-on support role, focused on incident resolution, user assistance and established operational procedures-not platform architecture, model development or infrastructure engineering.

Your future role within QRT

Provide first- and second-line support for QRT’s platform for accessing internally hosted and external LLMs, investigating issues raised by engineering and business users, maintaining support tickets and following 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.

Your present skillset

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.