AI Platform Architect | MLOps | AI Orchestration | Ph.D. in Computer Vision & Machine Learning
With 25 years of experience building software systems and over 15 years in AI, machine learning, and computer vision across academia and industry, I specialize in ML platforms, MLOps, and AI orchestration across cloud, HPC, and hybrid environments, helping organizations automate AI pipelines and move AI from research toward production.
AI Team Leadership @ ActiveEon (2017–2025)
At ActiveEon, I led PhD-level AI researchers and architected AI platforms for enterprise and industrial applications, orchestrating and automating research prototypes toward production. My responsibilities included:
- Led PhD-level AI researchers in AI/ML, Data, and Vision Systems
- Architected ProActive AI Orchestration (PAIO) for distributed AI workflows across hybrid cloud and on-premise environments
- Prototyped and deployed LLMs, deep learning, and AutoML pipelines across multi-node, multi-GPU, HPC, and hybrid cloud environments (please see the selected AI use cases below)
- Built custom SDKs and tools for dynamic workflow automation (e.g., ProActive Python SDK, ProActive Jupyter Kernel)
- Developed researcher-facing tooling for hyperparameter optimization, neural architecture search, and evaluation workflows
- Built a centralized MLOps Dashboard for model deployment, monitoring, observability, experiment tracking, and production AI operations
- Led the ExtremeXP (Horizon Europe) runtime implementation for scheduling and executing complex analytics workflows on distributed infrastructure, integrating distributed AutoML, resource monitoring, and dynamic service orchestration.
Selected Enterprise, Industrial & Research AI Use Cases
- (2025) LuxProvide — MeluXina Tier-1 Supercomputer — Designed and implemented reusable, parameterized workflow templates for the MeluXina supercomputer (SLURM-native execution), covering a) multi-node/multi-GPU distributed training with PyTorch DDP, b) AutoML-driven hyperparameter optimization integrated into the DDP training loop, c) MLflow-based experiment tracking with dynamic server provisioning, and d) LLM inference with vLLM across auto-provisioned Ray clusters.
- (2020-2023) IDRIS / Jean Zay (HPE partnership) — Built reusable distributed-training workflow templates for the Jean Zay supercomputer (CNRS/GENCI), enabling multi-node/multi-GPU TensorFlow/Keras training via two orchestration paths: a) SLURM-native execution, and b) Horovod-based workflow for multi-GPU training, both with Singularity/Docker containerization, automated hyperparameter search, and result tracking/visualization via TensorBoard.
Academic Background & Research
Ph.D. in Computer Vision & Machine Learning
Université de La Rochelle, France (2013-2017)
My research focused on computer vision, optimization, low-rank learning, tensor methods, and real-time video analytics, resulting in:
- 2800+ citations
- h-index: 17
- Publications in IEEE, Elsevier, Springer, CVIU, TIP, TNNLS, ICCV
- Reviewer for international journals and conferences
Open-Source & Contributions
I actively contribute to open-source software for computer vision and machine learning.
🔗 GitHub: github.com/andrewssobral
- BGSLibrary (C++) – Widely used background-subtraction library for moving-object detection.
- LRSLibrary (MATLAB) – Framework for low-rank/sparse decomposition.
- MTT (MATLAB) – Tools for tensor manipulation and decomposition.
- OSTD (MATLAB) – Online stochastic tensor decomposition for multispectral video.
- VDTC (C++ & Python) – Vehicle detection, tracking & counting pipeline.
- GoDec (Python) – Low-rank + sparse decomposition.
- DTT (C++) – Header-only library for seamless data type conversions (Eigen, OpenCV, Armadillo, LibTorch, ArrayFire).
These projects are used by universities, research labs, and industrial organizations worldwide.