Lead DevOps Engineer
Posted Updated
Summary
Lead DevOps Engineer (individual contributor, no direct reports) at London Stock Exchange, owning the technical direction of cloud infrastructure and delivery platforms that support AI products. Day to day: architecting and operating Azure/AWS platforms, building CI/CD and infrastructure as code, improving observability and incident response, and mentoring engineers using tools like Terraform, Kub
- We are building scalable AI products and platforms that require reliable cloud infrastructure, efficient delivery pipelines, and strong operational engineering. Join us and help shape the technical foundation behind our next generation of AI products!
- As a Lead DevOps Engineer, you will own the technical direction of our cloud infrastructure, delivery platforms, and operational capabilities. You will remain hands-on across the engineering lifecycle, from architecture and infrastructure design through deployment, observability, incident response, and continuous improvement
- We expect you to operate with a high degree of autonomy, make strategic and architectural decisions within your area, and resolve complex technical challenges. You will establish reusable engineering approaches that improve reliability, security, performance, and developer productivity
- This is an individual contributor role with no direct reports. You will provide functional leadership, guide engineers at different career stages, influence decisions across teams, and raise engineering standards
- Own the technical direction and roadmap for cloud infrastructure, DevOps, deployment, and operational capabilities
- Architect, build, and operate secure, observable, resilient, and scalable platforms across Azure, AWS, or both
- Design and improve CI/CD pipelines, infrastructure as code, automated testing, release controls, environment management, and deployment strategies
- Work with software engineering and data science teams to productionize AI solutions and ensure services are ready to operate reliably at scale
- Establish engineering standards and reusable patterns for infrastructure, security, observability, resilience, documentation, and operational readiness
- Lead architectural decisions and evaluate trade-offs across reliability, security, scalability, performance, cost, and maintainability
- Take ownership of operational risks and incidents, identify root causes, and turn lessons into measurable engineering improvements
- Use approved generative AI tools for infrastructure development, automation, testing, pipeline improvement, documentation, incident analysis, and troubleshooting
- Provide technical guidance through design reviews, code reviews, pairing, coaching, and hands-on problem-solving
- Career Stage: Manager
- Experience with Terraform, Bicep, CloudFormation, Ansible, or comparable infrastructure and configuration automation technologies
- Advanced experience with CI/CD, infrastructure as code, release automation, automated testing, deployment controls, and environment management
- Significant hands-on experience in DevOps, platform engineering, cloud infrastructure engineering, software engineering, or a related discipline
- Experience providing technical or functional leadership without formal management authority
- Clear communication skills and experience working across engineering, data science, security, architecture, and product teams
- The ability to work autonomously, make evidence-based architectural decisions, and take accountability for technical outcomes
- Demonstrable experience using generative AI tools in DevOps or engineering workflows, including validating outputs and protecting sensitive information
- Experience with containerized workloads and orchestration technologies such as Docker and Kubernetes
- Strong expertise in Azure, AWS, or both, including designing and operating production systems in complex cloud environments
- Knowledge of observability, incident management, cloud security, identity and access management, secrets management, network security, vulnerability management, and software supply-chain risk
- Strong software engineering and automation skills using Python, Bash, PowerShell, Go, or a similar language
- Experience with AI or machine learning platforms, model-serving infrastructure, MLOps, or data-intensive services
- Knowledge of advanced networking, API management, distributed systems, service mesh, or zero-trust architecture
- Experience with GitOps, policy as code, internal developer platforms, developer self-service, or FinOps
Skills
- AI
- Ansible
- API
- Automation
- AWS
- Azure
- Bash
- Bicep
- CI/CD
- Cloud
- Cloud Security
- CloudFormation
- Data Science
- DevOps
- Distributed Systems
- Docker
- FinOps
- Generative AI
- GitOps
- Infrastructure as Code
- Kubernetes
- Machine Learning
- MLOps
- Network Security
- Networking
- Observability
- PowerShell
- Python
- Secrets Management
- Terraform
- Test Automation
- Zero Trust