Why This Role Stands Out
This hybrid AI Platform Engineer role at Qube Research & Technologies offers a dynamic opportunity to shape cutting-edge LLM platforms, fostering significant career growth and skill development within a collaborative, technology-driven environment. You'll thrive here if you're a proactive problem-solver eager to ensure the stability and performance of critical AI infrastructure, contributing directly to innovative investment strategies. Apply now to join a reputable firm that values scientific rigor and continuous improvement.
Quick Overview
Job Description
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.
Your future role within QRT
- Provide first- and second-line support for LLM gateway platform, investigating and resolving issues raised by engineering and business users across the firm
- Monitor and maintain the platform's underlying infrastructure to ensure availability, stability and predictable performance under rapidly growing load
- Support and troubleshoot model-serving backends and provider integrations, model providers, covering latency, throughput, error-rate and capacity issues
- Triage incidents affecting model availability — provider instability, connection resets, timeouts, regional slowness — determine whether the cause is platform-side or upstream, and drive to resolution with vendors where required
- Support the tooling layer built on top of LLM gateway: integrations, developer workspaces (e.g. Coder), coding assistants and API clients, including diagnosing issues introduced by upstream vendor releases running against a gateway-fronted API
- Coordinate with platform engineering, cloud infrastructure and end-user teams to resolve incidents and minimise disruption
- Support release management and change processes to keep production stable, including staged rollouts, non-prod validation and rollback
- Build tooling and automation to improve monitoring, diagnostics and operational visibility, and to reduce repetitive manual work
- Contribute to the design and implementation of monitoring, dashboards and alerting — for example extending Grafana dashboards covering TTFT, TPOT, percentile latency and failure-rate reporting
- Own and improve operational documentation, runbooks and user-facing status communication
Your present skillset
- Experience in a production support, SRE or platform operations role within a fast-paced environment, with strong ownership of issue resolution end to end
Strong Linux and Windows system administration skills - Proficiency scripting and automating in Python, Bash and/or PowerShell
- Solid experience with relational databases such as PostgreSQL or SQL Server, including writing queries for investigation and supporting routine operational processes
- Practical understanding of monitoring and observability: metrics, logs, traces, dashboards and alerting, and the ability to analyse system data to distinguish a platform-wide problem from a localised one
- Comfortable debugging distributed, API-driven services: HTTP status and error semantics, timeouts, retries, connection resets, rate limiting, caching and latency percentiles
- Familiarity with large language model concepts and hosting environments — inference APIs, model gateways/proxies, prompt and context handling, token accounting, streaming responses, prompt caching
- Exposure to public cloud, ideally AWS (Bedrock, networking, IAM, logging/metrics), and to containerised or Kubernetes-based workloads
- Ability to communicate clearly with both engineers and non-technical users, and to manage expectations of senior stakeholders during live incidents
- Awareness of data-sensitivity and access-control considerations when routing workloads to third-party model providers
Beneficial
- Experience supporting developer tooling and AI coding assistants (e.g. Claude Code, OpenCode) or IDE/workspace platforms
- Experience with Grafana, Prometheus or equivalent observability stacks, including building dashboards and alert rules
- Experience with CI/CD and infrastructure-as-code (Terraform, Ansible, or similar)
- Experience operating multi-region services and troubleshooting region-specific performance issues (e.g. APAC latency)
- Experience acting as the operational interface to third-party vendors and cloud providers during degradations
QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance.
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