Cloud architecture
Multi-cloud and disaster recovery designs: AWS as the primary site with Azure for recovery, active/passive across zones, Kubernetes at scale, a 99.999% availability target.
I join teams for architecture, migration and performance work on AWS, Azure and Google Cloud. Every design choice comes with its monthly cost.
The same terms as the FinOps engineer, by the day: FinOps · régie.
Multi-cloud and disaster recovery designs: AWS as the primary site with Azure for recovery, active/passive across zones, Kubernetes at scale, a 99.999% availability target.
Rewriting the parts that cost the most, with the legacy system as the reference for every test until the numbers match.
Terraform, Terragrunt, Bicep and Ansible, with plan reviews, automated tests, policy as code and drift detection. Identity with Entra ID, OAuth/OIDC and SAML.
FinOps at the design stage rather than after the bill arrives: each option is priced before it is built.
Data extracted once to object storage as frozen columnar files. Short single-purpose steps that talk through storage, not to each other. Independent slices run in parallel on short-lived serverless functions on ARM, sized step by step.
An elastic ingestion and processing pipeline on AWS, designed to keep data intact and available at large scale, with the whole infrastructure replicated 1:1 on Azure, where a separate team owned disaster recovery. FinOps and DevOps were part of the design from the start, with infrastructure as code in Terraform, containers on Docker and Kubernetes, and CI/CD. Elasticity and zero interruption were bought with a much larger operational and security surface, which made NIS2 conformance load-bearing.
In the same engagement, a high-performance computing platform on AWS, designed and implemented as code: managed SLURM, FSx for Lustre, MPI and tightly coupled parallel jobs on an auto-scaled EC2 cluster.
Technical director of the engineering entity of a group of about 350 people. Standards, reference integration patterns and a development governance charter with three stated criteria: explicitness, simplicity, maintainability. ADRs made a habit rather than a mandate: teams got the format, the charts and protected time to write them. C4 introduced as the shared vocabulary across domains.
Managed frontier models for reasoning-heavy features, self-hosted lightweight Mistral models where data residency or per-token cost dominates. The trade-off: two runtimes and two security baselines, against lock-in and a token bill nobody could attribute. Cost per feature became a design input, and the reach of each data-processing agreement is decided at the boundary, per capability.
A self-service AI platform for developers and non-developers: more than 50 apps shipped to production, about 15 citizen developers onboarded. I run the technical architecture boards.
A support contract of about two days a month: took over an on-premise Hadoop data lab (6 nodes, about 40 daily data flows) and rebuilt the marketing ingestion.
Technical director of the engineering entity of a consultancy of about 350 people. More than 15 pre-sales, about ten of them won.
An R&D team of 15 people; 5 prototypes taken to production.
Successive engagements, ending with the rewrite of a simulation engine from C# to Rust: one run went from about 7 h 30 to under 30 seconds, and cloud costs fell by about half.
Hardware-level test benches and automated test plans in Python to validate rail safety systems such as tilting and emergency braking.
More than 50 short expert engagements: space and embedded systems, high-performance computing for finance, blockchain.
Pick a slot and tell me the context, dates and team. Prefer writing? contact@mathieukerbel.com