Cloud Platform Engineering Specialist

Overview

In this role you will lead the delivery of AI-native cloud platform programmes and design scalable infrastructure that powers data and AI workloads. You will shape AI-ready environments across multiple public clouds, champion AI-assisted delivery and automation, and mentor teams to uphold engineering excellence. The opportunity sits at the intersection of cloud engineering, AI, and platform governance within a global consulting practice. You will work with cross-functional teams to drive innovation and measurable impact for clients.

Pay / Benefits

• 25 days vacation per year
• private medical insurance
• 3 extra days’ leave per year for charitable work
• flexibility and mobility to spend time onsite with clients

Responsibilities

• Lead enterprise-scale cloud platform programmes and design AI-native infrastructure across Azure, AWS, GCP, Databricks, Microsoft Fabric and Snowflake
• Architect AI-ready platform environments from compute to security, tailored for GenAI, ML and agentic workloads
• Drive AIOps adoption with intelligent monitoring, automated incident response and self-healing capabilities
• Design and deliver CI/CD platforms with AI-assisted deployment, testing and automation
• Establish and govern Infrastructure as Code at scale, including AI-assisted code generation and governance
• Lead security, networking and compliance engineering, embedding policy-as-code and automated governance
• Design for resilience, FinOps and performance, leveraging AI-driven telemetry for optimization
• Promote agentic operations patterns and guide teams in autonomous/human-in-the-loop workflows
• Mentor platform engineering teams, set technical standards and foster continuous improvement
• Contribute to propositions, accelerators and reusable assets to shorten delivery time

Key requirements

• Design and delivery of enterprise-scale cloud platforms across Azure, AWS, GCP, Databricks, Microsoft Fabric or Snowflake
• Advanced IaC capabilities (Terraform, Bicep, CloudFormation or similar) with AI-assisted generation and governance
• CI/CD platform design and implementation with AI-assisted deployment and test automation
• AIOps, platform observability, and intelligent monitoring including self-healing capabilities
• Cloud security, networking, IAM, and policy-as-code engineering
• AI-ready platform architecture for GenAI, ML and agentic workloads in production
• Experience leading and mentoring engineering teams and setting technical direction
• Familiarity with agentic AI patterns and governance implications

• leadership
• mentoring
• collaboration
• Azure
• AWS
• GCP

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