Representative areas of engineering experience. Environments are described without customer names.
High-volume consumer technology platform
Designed and implemented Kubernetes-based infrastructure for machine-learning and generative-AI workloads — serving, routing, rollout control, and observability as one platform path.
Multi-account cloud engineering organization
Designed and automated a shared cloud baseline — identity, networking, Kubernetes, and reusable foundations — so application teams could build without owning the underlying stack.
Fintech product engineering team
Built Terraform and CI/CD as the only path to create environments, so infrastructure changes were reviewable, repeatable, and owned by the team after handover.
Public-facing production application, high request volume
Rebuilt how production was observed, shipped, and recovered — telemetry that can be acted on, safer releases, and a defined path when things fail.
Multi-account enterprise AWS estate
Implemented identity and access-control patterns in the same infrastructure as the platform, tightening boundaries and cutting standing access paths.
Product engineering team planning a production AI path
Advised on how to serve, route, and operate AI workloads — including self-hosted inference options, failover, and production constraints. Design and guidance, not a claim of completed production operation.
TEDEAS works with engineering organizations that have outgrown ad-hoc infrastructure but do not want a big-firm engagement.
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