Platform Solution Architect (IaC and Dev)

Posting Start Date: 9 Jun 2026

Location: Prague, CZ

Company: Serco Plc

Purpose of the Role

Define the architecture and engineering patterns for the core platform that other teams build on, including the AI platform foundation.

This is a hands-on architecture role spanning infrastructure-as-code, deployment standards and operational design.

Key Responsibilities

  • Define reference architectures and reusable IaC patterns for Azure workloads, and configuration-as-code patterns for Microsoft 365.
  • Set standards for CI/CD, GitOps, secret rotation, and image registry strategy.
  • Design observability and operational tooling that delivery teams plug into from day one.
  • Review incoming workloads (including the AI platform) for fit with platform and security standards.
  • Partner with engineers to keep architecture hands-on and implementable, not just documented.
  • Take an AI-first approach to the work, using agentic AI tooling as the default way of building and delivering.
  • Work in an iterative, board-tracked flow (Azure DevOps Boards or GitHub Projects), with peer code review as standard practice.
  • Keep clear architecture and technical documentation current as part of the definition of done.

Required Skills and Experience

  • Hands-on solution or platform architecture experience on Azure with a strong IaC focus (Terraform or Bicep) in large corporation/organisation.
  • Hands-on engineering background; comfortable writing and reviewing infrastructure code.
  • Deep knowledge of AKS, networking, identity and Azure security patterns.
  • Experience defining CI/CD and GitOps standards for multiple teams.
  • Ability to balance standardisation with enabling delivery teams.
  • Comfortable challenging technical decisions and proposing future-proof solutions, open to market innovation.
  • Education: degree in Computer Science or similar.
  • Language: Good English language skills (Mandatory)

Nice to Have

  • Experience designing for scale-to-zero and cost-efficient operations.
  • Exposure to AI or data platform workloads.
  • FinOps-aware architecture decisions.