About Us
LSEG (London Stock Exchange Group) is more than a diversified global financial markets infrastructure and data business. We are dedicated, open-access partners with a dedication to excellence in delivering the services our customers expect from us. With extensive experience, deep knowledge and worldwide presence across financial markets, we enable businesses and economies around the world to fund innovation, manage risk and create jobs.
Role Summary
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.
What You'll Be Doing
- 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.
What You'll Get in Return
- High-impact work: We build AI products and large-scale data services that address valuable and technically complex problems.
- Technical ownership: We will give you meaningful scope to shape cloud architecture, engineering standards, platform priorities, and ways of working.
- Experienced teams: We bring together software engineers, data scientists, security specialists, product partners, and technical leaders to deliver dependable systems.
- Continued development: We support continuous learning, deeper technical specialization, and broader architectural responsibility.
- LSEG offers a range of tailored benefits and support, including healthcare, retirement planning, paid volunteering days and wellbeing initiatives.
What You'll Bring
- Significant hands-on experience in DevOps, platform engineering, cloud infrastructure engineering, software engineering, or a related discipline.
- Strong expertise in Azure, AWS, or both, including designing and operating production systems in complex cloud environments.
- 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.
- The 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.
Bonus points
- 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.
