AI DevOps Engineer

Overview

A leading international financial institution is undertaking a large-scale transformation across Markets Technology, driven by a combination of infrastructure modernisation, application migration and a broader push to improve engineering efficiency.

As part of this initiative, a centralised DevOps capability is being established to support the migration and standardisation of a complex estate of applications, while introducing more efficient and scalable ways of working across teams.

This role will sit within a newly formed central team, working across multiple Markets Technology groups to accelerate delivery, reduce duplication and improve the end-to-end software delivery lifecycle.

Project Context

The team will support a multi-year programme focused on:

• Migration of approximately 90+ applications across Markets Technology

• Transition to new data centre environments as part of the GDC West programme

• Adoption of a standardised internal Kubernetes platform

• Migration to new object storage solutions and infrastructure models

• Replacement of existing workflow and scheduling tools (including Control-M) with modern alternatives such as Airflow

• Consolidation of DevOps practices across teams to avoid duplication and inconsistency

In parallel, the organisation is introducing AI tooling within the engineering lifecycle. The objective is not to apply AI in isolation, but to use it pragmatically to remove bottlenecks, improve efficiency and scale successful approaches across the wider estate.

Role Responsibilities

• Work across multiple application teams within Markets Technology to support platform migration and standardisation efforts

• Identify inefficiencies within the software delivery lifecycle and help design practical solutions to address them

• Build and promote reusable DevOps patterns that can be applied consistently across teams

• Contribute to migration activities, including application replatforming and workflow orchestration changes

• Use AI tooling where appropriate to accelerate repeatable engineering tasks and capture best practice

• Collaborate closely with central platform and infrastructure teams, particularly in relation to Kubernetes and internal tooling

• Support knowledge sharing across teams to ensure consistent adoption of new practices

• Operate effectively across team boundaries, ensuring alignment rather than duplication of effort

Technical Requirements

• Strong DevOps or platform engineering experience within a large, complex environment

• Solid exposure to Java-based application ecosystems

• Experience deploying and managing applications on Linux (preferably Red Hat)

• Experience working with Kubernetes in an enterprise setting

• Hands-on experience with CI/CD tooling (Azure DevOps preferred)

• Experience supporting multiple applications or working across teams rather than within a single isolated product

• Experience with workflow tools such as Control-M or Airflow

• Experience automating DevOps processes and workflows using AI

AI and Engineering Efficiency

The team will make use of AI tooling, including GitHub Copilot and similar technologies, to improve engineering productivity.

Candidates are not expected to be specialists in AI, but should be comfortable adopting new tools and applying them to real engineering challenges where they provide measurable value. The focus is on practical application rather than theoretical knowledge.