Cédric Sazos

Contributed to a high-performance 3D data analysis project for Airbus,

specializing in the processing and algorithmic optimization of massive geometric

datasets. This role required a deep understanding of memory efficiency, spatial

data structures, and low-latency code execution.

Key Responsibilities & Achievements:

- 3D Geometric Data Analysis: Developed algorithms to analyze complex 3D data

models for Airbus, successfully extracting exact geometries and key

engineering metrics such as piping networks, nozzles, and internal volumes.

- Algorithmic Optimization: Re-engineered and optimized processing pipelines to

handle massive 3D point clouds containing several million points,

significantly reducing execution time and computational overhead.

- High-Performance Code Practice: Focused on writing highly efficient,

CPU-optimized code to manipulate dense spatial data frames without causing

system bottlenecks.

- Agile Team Integration: Actively participated in all daily sprints, review

meetings, and retrospective sessions within a fast-paced team collaborating

under the Scrum agile methodology.

Technical Stack Used:

- Languages & Tooling: C++, Geometric algorithms

- Frameworks: Agile (Scrum)

- Target Industry: Aerospace & Industrial 3D Modeling (Airbus project)

As the DevOps Lead for Opensource-Together—a collaboration platform dedicated to scaling emerging Open Source projects—I took full ownership of the infrastructure, CI/CD automation, and application reliability, while collaborating on the NestJS backend.

Key Responsibilities & Achievements:

Infrastructure & CI/CD: Designed and implemented automated CI/CD pipelines using GitHub Actions and Docker, managing seamless deployments across self-hosted production and staging environments.

Observability & Monitoring: Deployed a comprehensive monitoring and logging stack using Grafana Cloud and Loki, enabling proactive incident response and system visibility.

High Availability: Maintained a proven 99.87% uptime over 5 months in production through robust error handling, monitoring, and stable AWS S3 integrations.

Scalability & Evolution: Conducted the architectural study and roadmap for migrating the core API and heavy ML pipelines to Kubernetes to achieve elastic auto-scaling.

Developed a lightweight, high-concurrency URL minification service built entirely in Go. Focused heavily on writing clean, idiomatic Go code, optimizing HTTP request handling, and ensuring minimal memory footprints. Implemented efficient routing and data persistence layouts to handle high-throughput redirects.