About This Role
6 to 10 years of experience
Skills: DevOps, CI/CD, Python, Kubernetes, Docker, AWS / Azure, Terraform, GitLab, Quant Libraries, Risk Systems, Market Data, Linux, Shell Scripting, Jenkins, Ansible, Performance Monitoring
About the Role
We are looking for a DevOps Engineer with 6–10 years of experience and strong exposure to quantitative finance technology to join our Quant Technology team. You will design, build, and operate the CI/CD pipelines, cloud infrastructure, and automation frameworks that underpin our quantitative research, pricing, risk, and trading platforms. You will bridge the worlds of DevOps engineering and quantitative finance technology — ensuring that quant models, risk systems, and market data platforms are delivered, deployed, and operated with the reliability, performance, and security demanded by a regulated financial services environment.
What We're Looking For
- 6–10 years of DevOps / Platform Engineering experience with proven exposure to quantitative finance, capital markets, or financial services technology environments.
- Strong CI/CD expertise — GitLab CI, Jenkins, or GitHub Actions — with experience building and managing pipelines for complex, multi-component financial technology systems.
- Good understanding of quantitative finance technology — pricing engines, risk systems, market data platforms, and quant library management — sufficient to engage credibly with quant developers and researchers.
- Deep Kubernetes skills — EKS / AKS administration, Helm, GitOps, RBAC, and workload performance tuning for HPC and latency-sensitive financial applications.
- Strong Python and Shell scripting skills for infrastructure automation, quant environment management, and operational tooling.
- Solid Terraform expertise — modular IaC design, remote state management, and cloud resource provisioning for financial services environments.
- Strong AWS and / or Azure experience — cloud infrastructure management, HPC services, security configuration, and cost optimisation for quantitative workloads.
- Experience with observability platforms — Prometheus, Grafana, Datadog, or Splunk — for comprehensive quant technology monitoring and incident response.
- Solid understanding of financial services regulatory and security requirements — SOX, MiFID II, APRA — and their implications for DevOps practices and change governance.
- Excellent communication skills — able to engage effectively with quantitative analysts, developers, risk managers, and senior technology stakeholders.
Nice to Have
- Experience with kdb+ / q — time-series database used extensively in quant finance for tick data management and analytics.
- Familiarity with algorithmic trading infrastructure — order management systems (OMS), execution management systems (EMS), and FIX protocol environments.
- Knowledge of GPU infrastructure management — NVIDIA CUDA, GPU cluster administration — for deep learning and quant model acceleration.
- Exposure to QuantLib, OpenGamma, or equivalent open-source quant libraries for pricing and risk analytics.
- Experience with low-latency networking — DPDK, RDMA, kernel bypass — for ultra-low-latency quant application infrastructure.
- Familiarity with Apache Kafka or Solace for real-time market data distribution and event-driven quant platform architecture.
- AWS Certified DevOps Engineer Professional or Azure DevOps Engineer Expert certification.
- Certified Kubernetes Administrator (CKA) or Certified Kubernetes Security Specialist (CKS).
- HashiCorp Certified: Terraform Associate.
- Experience in Investment Banking, Hedge Fund, Asset Management, or equivalent quantitative finance environment.
Key Responsibilities
CI/CD & Pipeline Engineering
- Design, build, and maintain CI/CD pipelines for quantitative finance applications — including pricing libraries, risk calculation engines, quant model deployments, and market data platform components.
- Implement release automation and deployment strategies — Blue-Green, Canary, and Rolling deployments — with automated rollback for quant system releases that demand zero-downtime delivery.
- Manage artifact versioning and dependency management — ensuring quant libraries, model binaries, and configuration artefacts are versioned, traceable, and reproducible across environments.
- Integrate security scanning (SAST / DAST) and compliance checks into quant technology pipelines — enforcing regulatory and security standards before code reaches production.
- Maintain environment parity across Dev, UAT, and Production — ensuring quant models and risk systems behave consistently across all deployment targets.
Quantitative Technology Infrastructure
- Manage and support quantitative finance infrastructure — pricing engines, risk calculation platforms, backtesting frameworks, and market data distribution systems.
- Support quant library dependency management — QuantLib, NumPy, SciPy, pandas — ensuring consistent, versioned library environments across research, development, and production.
- Manage market data platform integrations — Bloomberg, Refinitiv / LSEG, ICE — including feed management, data normalisation pipelines, and platform availability monitoring.
- Support high-performance computing (HPC) workloads — managing grid computing, parallel processing, and distributed calculation frameworks for risk and pricing computation.
- Implement and maintain low-latency infrastructure optimisations — network tuning, kernel parameter management, and CPU affinity configuration — for latency-sensitive quant applications.
- Collaborate with quantitative analysts and developers to deploy, version, and govern quant model releases across research and production environments.
Cloud & Infrastructure
- Architect and manage cloud infrastructure for quant technology workloads on AWS and / or Azure — leveraging EKS / AKS, HPC services, and managed data services for scalable, cost-efficient quant computing.
- Provision and manage infrastructure using Terraform — developing reusable, modular IaC configurations for quant technology environments.
- Manage hybrid cloud architecture — integrating on-premise quant infrastructure with cloud-based HPC, data storage, and model serving platforms.
- Optimise cloud resource usage for quant workloads — implementing auto-scaling, spot instance strategies, and FinOps practices to manage HPC compute costs.
Monitoring & Reliability
- Implement comprehensive observability for quant technology platforms — Prometheus, Grafana, Datadog, or Splunk — with dashboards and alerting for pricing engine performance, risk calculation SLAs, and market data feed health.
- Monitor low-latency quant application performance — tracking tick-to-trade latency, calculation throughput, and system resource utilisation to proactively identify and resolve performance bottlenecks.
- Define and track SLOs for quant technology services — ensuring pricing, risk, and market data platforms meet availability and performance commitments aligned to trading and regulatory requirements.
- Lead incident response for quant technology outages — coordinating rapid resolution of pricing engine failures, market data outages, and risk system incidents with minimal business impact.
Security, Compliance & Governance
- Implement DevSecOps practices across quant technology pipelines — integrating vulnerability scanning, secrets management, and compliance gates aligned to financial services regulatory requirements.
- Manage secrets and credential lifecycle across quant platforms — HashiCorp Vault, AWS Secrets Manager — ensuring secure handling of market data credentials, API keys, and system certificates.
- Support regulatory compliance activities — SOX, MiFID II, APRA — producing audit evidence for change management, deployment governance, and access control across quant technology environments.
- Enforce change management processes for quant system releases — coordinating with CAB, risk, and compliance teams to ensure quant platform changes meet governance requirements
