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About me 👋
My name is Adrien, a dedicated Software Engineer from Luxembourg, specializing in MLOps and data-intensive systems.
🚀 As a Senior MLOps Engineer, I architect, build, and implement comprehensive MLOps platforms from the ground up, primarily for enterprise clients in the financial sector. My core focus is on enabling robust, scalable, and automated machine learning lifecycles – from development and training through to deployment and monitoring in production.
Key areas of my MLOps expertise include:
- Designing cloud-agnostic infrastructure for AI model development and deployment (training & inference).
- Implementing automated provisioning of cloud resources and CI/CD pipelines for models and data.
- Integrating model registries (e.g., MLflow) and data versioning tools (e.g., DVC).
- Leveraging Kubernetes for scalable model deployment and developing custom gateways (Go) for enhanced security, monitoring, and load management.
- Optimizing data scientist workflows and managing on-premise GPU resources effectively.
Interests & Continuous Learning ⚙️
Beyond my professional MLOps work, I possess a strong intellectual curiosity for High-Performance Computing (HPC) and low-level systems programming. I am fascinated by the intricacies of how software interacts with hardware to achieve maximum efficiency.
This passion drives my current learning endeavors in:
- Zig: Exploring its potential for systems programming and performance-critical applications.
- Memory Management: Deepening my understanding of manual and automatic memory allocation strategies.
- Parallel Computing: Actively studying frameworks like OpenCL, CUDA, and potentially OpenGL for GPGPU, to harness parallel processing power.
- Investigating compilers, assembly language, and hardware architecture to gain a foundational understanding of system performance.
🎯 My ambition is to lead the design of cutting-edge MLOps solutions and contribute to complex, performance-critical open-source projects, ideally in a remote capacity.
📅 I am committed to daily coding and continuous learning through technical literature and community engagement.
🎲 Fun fact: I can fall asleep in <1min every night.