About Asite

We start with a simple idea: the built environment should be smarter, safer and more sustainable. Everything we do is about helping the people behind major construction and infrastructure projects work together more easily and make better decisions.

Asite offers a cloud-based platform that connects project teams, improves collaboration and manages data from the first design to the final handover. Industry leaders such as Laing O’Rourke, Transport for London and Aldar use Asite to keep their projects running smoothly and delivering strong results.

With offices around the world and a record of steady, profitable growth, we are shaping the future of construction technology while supporting the people who build the world around us.

ABOUT THE ROLE

As a DevOps Engineer at Asite, you will design and implement our cloud infrastructure, CI/CD pipelines, and monitoring and alerting systems. You will work closely with our development team to ensure that software is deployed efficiently, securely, and with high quality, leveraging modern automation and AI-assisted tooling wherever it improves speed and reliability, and you will collaborate closely with the Asite Platform Operations team.

You will have the opportunity to work on cutting-edge technology and make a real impact on the construction industry, joining an engineering organisation that values automation, reliability, and continuous improvement.

KEY RESPONSIBILITIES

–   Design, implement, and maintain our cloud infrastructure on Azure using Infrastructure as Code (Terraform/Bicep).

–   Implement and manage CI/CD pipelines using Jenkins, Azure DevOps and GitHub Actions.

–   Develop and maintain monitoring, logging, and alerting systems using the ELK/Elastic stack and the Grafana/Loki/Prometheus stack.

–   Build observability using modern standards (e.g. OpenTelemetry) for distributed tracing and metrics.

–   Collaborate with the development team to ensure efficient deployment and testing of software.

–   Evaluate and integrate AI-assisted DevOps tools (e.g. AI-driven anomaly detection, AIOps for incident response, Copilot-style coding/automation assistants) to improve efficiency and reduce manual toil.

–   Troubleshoot and resolve technical issues in a timely and efficient manner, using AI-assisted root-cause analysis tools where useful.

–   Continuously improve infrastructure and processes for scalability, reliability, cost-efficiency, and security (DevSecOps mindset).

REQUIREMENTS

Must have

–   Excellent problem-solving and analytical skills.

–   Strong communication and collaboration skills.

–   Expertise in administration of Linux/Ubuntu server environments.

–   Experience with containers and container orchestration (Docker, Kubernetes).

–   Experience with scripting/automation languages (Bash, Python, or similar); comfort using AI coding assistants (Copilot, Claude, etc.) to accelerate scripting and troubleshooting.

–   Experience with Infrastructure as Code (Terraform, Bicep, or ARM templates).

–   Experience with setup and maintenance of monitoring/observability systems.

–   Experience managing a public cloud environment (Azure preferred).

–   Strong understanding of networking, cloud security, and DevSecOps principles (SAST/DAST, secrets management, container/image scanning).

–   Familiarity with Git and modern branching/GitOps workflows.

–   Familiarity with database deployment and management.

–   Familiarity with Agile methodologies and DevOps practices.

Nice to have

–   Proven experience as a DevOps Engineer in a cloud-based environment.

–   Expertise in Kubernetes (AKS), Helm, Docker, and Nginx.

–   Familiarity with Microsoft Azure, including cost analysis/FinOps for Azure deployments.

–   Familiarity with MongoDB and RabbitMQ.

–   Familiarity with the Elastic Stack and Grafana/Loki/Prometheus.

–   Exposure to AI/ML-driven observability or AIOps platforms.

–   Experience integrating LLM-based tools into CI/CD or support workflows (e.g. automated PR reviews, chatbot-assisted troubleshooting, log summarization).

–   Awareness of security considerations for AI-integrated pipelines (model/data governance, prompt injection risk in automated tooling).

Educational qualifications

–   Bachelor's degree in Computer Science, Engineering, or a related discipline, or equivalent practical experience.