Qube Research & Technologies
Financial Services
DevOps/PlatformEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“DevOps / Platform Engineer at Qube Research & Technologies. Skills: Platform Engineering, AWS, Kubernetes, DevOps. Build infrastructure for AI platform. Operate infrastructure for AI platform”
Industry & Context.
Ownership-driven approach
What They're Looking For.
Must Have
4+ years Cloud/DevOps/Platform Engineering, Kubernetes expertise, Hands-on AWS experience, Python skills, Experience with infrastructure-as-code, Experience building CI/CD workflows, Experience defining infrastructure SLOs
Nice to Have
Experience supporting LLM inference, Experience with model serving platforms, Experience with GPU-backed infrastructure, Familiarity with RAG systems, Familiarity with vector databases, Familiarity with AI data platforms, Experience building internal APIs, Experience building platform services, Understanding of agentic AI architectures, Experience in latency-sensitive environments, AWS certification, Kubernetes certification
What You'll Do.
Build infrastructure for AI platform
Operate infrastructure for AI platform
Manage Kubernetes environments
Manage AWS environments
Support model serving
Support observability
Support developer tooling
Own core infrastructure services
Shape AI capability delivery
Design infrastructure across AWS
Design infrastructure on-premise Kubernetes
Support LLM workloads
Manage Kubernetes scheduling
Manage Kubernetes multi-tenancy
Manage Kubernetes resource isolation
Manage GPU-backed workloads
Develop hybrid cloud strategies
Balance data residency
Own AWS cost management
Build infrastructure as code
Maintain infrastructure as code
Use version-controlled tooling
Implement CI/CD workflows
Maintain CI/CD workflows
Implement GitOps workflows
Maintain GitOps workflows
Build observability solutions
Maintain observability solutions
Automate capacity management
Enforce authentication
Enforce rate limiting
Enforce cost attribution
Define infrastructure SLOs
Operate against infrastructure SLOs
Partner with AI Platform Engineers
Support inference workloads
Enable internal teams
Provide platform tooling
Provide self-service capabilities
How You'll Work.
Team & Collaboration
AI Platform Engineers; Researchers; Data Scientists
Full 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. As a DevOps / Platform Engineer, you will build and operate the infrastructure that powers QRT's internal AI platform. You will be responsible for the Kubernetes and AWS environments supporting model serving, observability, and developer tooling, ensuring they remain reliable, secure, and scalable. Working closely with AI Platform Engineers, Researchers, and Data Scientists, you will own core infrastructure services and help shape how AI capabilities are delivered across the firm. Your future role within QRT: Infrastructure & Cloud Design and operate infrastructure across AWS and on-premise Kubernetes environments supporting AI and LLM workloads Manage Kubernetes clusters, including scheduling, multi-tenancy, resource isolation, and GPU-backed workloads Develop hybrid cloud strategies balancing performance, cost, and data residency requirements Own AWS infrastructure, including networking, IAM, security, and cost management Build and maintain infrastructure as code using reusable, version-controlled tooling Platform Operations Implement and maintain CI/CD and GitOps deployment workflows Build observability solutions covering system health, utilisation, latency, and platform performance Automate scaling and capacity management Enforce authentication, rate limiting, auditability, and cost attribution across platform services Define and operate against infrastructure SLOs Collaboration Partner with AI Platform Engineers to support model serving and inference workloads E
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