Cloud & DevOps Engineering
We make your infrastructure boring in the best way — automated, observable, and reliable — so your team ships faster and sleeps better.
Slow releases, flaky environments, and 2am incidents are usually infrastructure problems wearing a product costume. Fixing them frees your engineers to build.
We modernise cloud infrastructure and DevOps practices: automated CI/CD, infrastructure-as-code, containerisation and Kubernetes, observability, and the MLOps foundations that AI workloads specifically need.
What's included
- CI/CD pipeline design and automation
- Infrastructure-as-code (Terraform) across AWS, GCP, Azure
- Containerisation and Kubernetes orchestration
- Observability: logging, metrics, tracing, alerting
- MLOps: model serving, monitoring, and retraining pipelines
- Cost optimisation and reliability / SRE practices
How we approach it
- Audit current pipelines, environments, and incident history
- Automate the highest-pain, highest-frequency toil first
- Codify infrastructure and add observability
- Harden for scale with SRE and cost controls
What you get
- Automated, documented CI/CD pipelines
- Infrastructure-as-code for reproducible environments
- Monitoring dashboards and alerting
- A measurable drop in deployment time and incidents
Technologies we use
Frequently asked questions
Do you work across multiple clouds?
Yes — we work across AWS, GCP, and Azure, and design portable, infrastructure-as-code setups that reduce lock-in where it matters.
Can you support AI/ML workloads specifically?
Absolutely. GPU scheduling, model serving, and MLOps pipelines are a core part of what we do — infrastructure built for AI, not retrofitted.
Ready to talk cloud & devops?
Tell us about your project and we'll respond within 24 hours with a clear next step.