Qube Research & Technologies

Financial Services

DevOps/PlatformEngineer

$185–275k ~AI est. Wrocław, Lower Silesian Voivodeship, Poland
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“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.

Financial Services
Problems you'll solve

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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