AI DevOps Engineer

Department Overview:

Our Technology Infrastructure team operates globally and is responsible for every aspect of the firm's hybrid platform. This ranges from our EUC/Office environments to Trading and Core service Co-Location Data Centres, and extends to Public Cloud, delivering top-tier technology services to a dynamic and demanding Trading organisation.

In addition to meeting the round-the-clock operational demands of the platforms, we continuously evolve and transform our platforms to maintain a competitive edge that our business requires. We innovate to provide valuable solutions and leverage our skilled Technology teams to deliver against rapidly changing business requirements.

Role Overview: 

The AI DevOps Engineer is a very technical, hands-on role, responsible for end to end delivery and operations of our DevOps environments. They will be joining the team as we lead an ambitious technology modernisation journey, moving to greater adoption of AI, microservices and public cloud. We’re continuing to evolve our global infrastructure - spanning multiple global data centres - into a modern hybrid architecture that leverages both existing on-prem and multi-cloud environments and capabilities across Azure and AWS.

Over the past four years, we've modernised our core trading systems and introduced a range of key technologies including AI, Kubernetes, Kafka, ELK, Prometheus, and more. As we expand and enhance the platforms, our focus is on automating and standardising to ensure consistency, scalability and reliability across all environments.

Experiences and Skills Required: 

Must have:

• Strong object oriented Python skills (not just scripting)
• Excellent understanding of containerisation technologies, ideally Kubernetes
• Strong experience with GitHub Actions
• Infrastructure as Code (IaC):
  • Good, enterprise experience with Terragrunt, Terraform, and Ansible
• Delivery-focused mindset, with priorities aligned to business needs.

Nice to have, but willing to learn otherwise:

• Experience building and deploying AI agents
• Expertise across Azure and AWS, with a solid understanding of AI-native services:
  • Experience with Azure AI Foundry and Amazon Bedrock
  • Cloud automation experienceAgentOps/DataOps frameworks (e.g. LangGraph, Dagster) and OpenTelemetry
• Predictive operations tools (e.g. Prophet, Dynatrace)
• AI tool integration, including embedding LLM APIs and agents into pipelinesExperience integrating DevOps infrastructure with MCPs