Designing and operating Kubernetes clusters across AWS, Azure, and GCP.
Cluster design and multi-cloud operations, GPU infrastructure for AI/ML workloads, Helm-based deployment standardisation, and consistent, repeatable release processes.
Multi-cloud infrastructure built for cost-efficiency, security, and scale.
AWS, Azure, GCP, and specialised AI compute providers (Nebius AI, RunPod, DigitalOcean); VPC/VNet design; IAM; disaster-recovery planning that covers every system by default.
Automated provisioning, end-to-end, with zero manual drift.
Terraform, Ansible, ARM templates, and CloudFormation, used to keep environments consistent, auditable, and fast to change across dev, staging, and production.
Deployment pipelines that ship safely, every time.
Jenkins, GitHub Actions, Azure DevOps, and Argo CD, including CI/CD purpose-built for ML models, cutting inference deployment time by 25%.
Security that doesn't depend on someone remembering to run a scan.
SAST, DAST, SCA, secrets scanning, and policy-as-code embedded as mandatory pipeline gates; TLS/SSL and Cloudflare CDN/DDoS protection for production services.
Production-grade infrastructure for machine learning, not just notebooks.
GPU utilisation and cost-effective compute, model inference optimisation (TensorFlow, SageMaker, RunPod AI), and AI models served as resilient, load-balanced microservices.
Systems that tell you what's wrong before your customers do.
Prometheus, Grafana, Datadog, and Azure Monitor stacks paired with real disaster-recovery practice, incident runbooks, and postmortems that actually get read.
Cloud spend that reflects what the business actually needs.
A consistent track record of cutting cloud costs by 20 to 30%+ through right-sizing, workload rebalancing, and automation, without sacrificing reliability or performance.