Lead DevOps Engineer
London Stock Exchange Group2 hours ago
London, United KingdomStaff+
Responsibilities
- 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.
- Partner with software engineering and data science teams to productionize AI solutions and operate services reliably at scale.
- Establish reusable standards and patterns for infrastructure, security, observability, resilience, documentation, and operational readiness.
- Lead architectural decisions and evaluate reliability, security, scalability, performance, cost, and maintainability trade-offs.
- Own operational risks and incidents, identify root causes, and implement 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.
Requirements
- Significant hands-on experience in DevOps, platform engineering, cloud infrastructure engineering, software engineering, or a related discipline.
- Strong expertise designing and operating production systems in Azure, AWS, or both.
- Advanced experience with CI/CD, infrastructure as code, release automation, automated testing, deployment controls, and environment management.
- Experience with Terraform, Bicep, CloudFormation, Ansible, or comparable infrastructure and configuration automation technologies.
- Experience with containerized workloads and orchestration technologies such as Docker and Kubernetes.
- Strong software engineering and automation skills using Python, Bash, PowerShell, Go, or a similar language.
- Knowledge of observability, incident management, cloud security, identity and access management, secrets management, network security, vulnerability management, and software supply-chain risk.
- Demonstrable experience using generative AI tools in DevOps or engineering workflows, including validating outputs and protecting sensitive information.
- Ability to work autonomously, make evidence-based architectural decisions, and take accountability for technical outcomes.
- 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.
- Preferred experience with AI or machine learning platforms, model-serving infrastructure, MLOps, or data-intensive services.
- Preferred knowledge of advanced networking, API management, distributed systems, service mesh, or zero-trust architecture.
- Preferred experience with GitOps, policy as code, internal developer platforms, developer self-service, or FinOps.
Benefits
- High-impact work on AI products and large-scale data services.
- Meaningful technical ownership over cloud architecture, engineering standards, platform priorities, and ways of working.
- Collaboration with software engineers, data scientists, security specialists, product partners, and technical leaders.
- Support for continuous learning, technical specialization, and broader architectural responsibility.
- Healthcare, retirement planning, paid volunteering days, and wellbeing initiatives.
- Equal opportunity employment with reasonable adjustments and accommodations throughout the recruitment process.