Adrien Bouvais

Data & Platform Engineer based in Luxembourg.
I design backend systems, data platforms, and inference infrastructure — currently architecting the backend for Clearstream AI at Clavem Group.

Work

Senior Data/ML Engineer — Clavem Group (2024 – present)
Lead backend architect for a cloud-agnostic AI/data platform serving multiple business units in production.

Designed full platform architecture: ingestion, storage, serving, and security layers
GitOps CI/CD on OpenShift/ArgoCD — config repos per environment, PR-gated promotion to production
Inference containers in Python, model/dataset registry via DVC + Artifactory, automated via GitHub Actions
Operated and monitored production workloads on OpenShift; enforced secrets management and least-privilege access

Data Engineer — Reveals SA (2023 – 2024)
Core banking data migration and quality platform for a Luxembourg bank.

Fault-tolerant pipeline migrating billions of rows of sensitive financial data — zero data loss, TLS-secured
Airflow DAGs for multi-source ETL: parallel tasks, dataset dependencies, Oracle → PostgreSQL consolidation
Real-time data quality platform (Python/Flask) and self-service SQL validation framework for non-technical stakeholders

Data & AI Consultant — Ernst & Young (2022 – 2023)
Data architecture advisory for enterprise clients on Azure.

Designed data platform architectures and ETL pipelines for clients across multiple industries
SQL schema optimisation for analytical workloads; Airflow-orchestrated data flows on Azure

Currently Building

Zigma — A Programming Language for Science

Write physics equations as code. Zigma attaches dimensions and units directly to variables and resolves them at compile-time via Zig's comptime — so a unit mismatch is a compiler error, not a runtime catastrophe.

g  = 9.81 m/s²
t  = 0..10 s  step 0.1
v0 = 20.0 m/s

y = (v0 * t) - (0.5 * g * t^2)  // Zigma derives: y is in [meters]

wrong = y + t  // Compile-Time Error: Cannot add [Length] to [Time]

Zigma transpiles directly to Zig — C-level performance, automatic SIMD acceleration, zero runtime overhead. Supports uncertainty propagation (20.0 +/- 0.5 m/s), native CSV interpolation for empirical data, and i128 precision for large-scale spatial simulations.

Roadmap: WASM notebook, GPU acceleration (WebGPU), autograd, automatic Jacobian transforms.

dimal — Dimensional Analysis Library for Zig

The type system powering Zigma. A unified Tensor API parameterized entirely at compile-time:
T (numeric type) · dims (physical dimensions) · scales (unit scale) · shape (array shape)

Originally built to use i128 positions in space simulations to avoid float precision loss at astronomical scales.

Stack

Python Go Zig SQL · PostgreSQL Oracle SQLite
OpenShift ArgoCD Airflow Terraform GCP Azure
GitHub Actions DVC FastAPI Flask Docker

contact@bouvais.lu