ABOUT ASITE

Asite is the digital construction platform trusted by the organisations that build at scale. Our vision is simple: connect people everywhere and help the world build better – faster, smarter, with fewer failures and less rework. We connect the physical, digital, and human dimensions of construction, making project information actionable, auditable, and secure at every stage of the asset lifecycle – from feasibility and design through construction, handover, and operations.

From 3D modelling and secure agentic AI to document control and field collaboration, Asite works across every discipline and every tier of the supply chain. We help clients meet the standards that matter, including Golden Thread reporting under the Building Safety Act and ISO 19650 – so the right information is always traceable and available to the people who need it.

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.

Experience Level: 3+ years

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.

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