About
AI Architect | Former Head of AI at ActiveEon | Ph.D. in Computer Vision & Machine Learning
20+ years in Software Engineering • 10+ years in AI, Computer Vision, Deep Learning, and Distributed Systems
Expert in AI at Scale, HPC+AI, MLOps, Computer Vision, Edge AI, Generative AI, and LLM Optimization.
👉 Currently open to senior AI leadership and consulting opportunities.
I am an AI Architect and Researcher with a strong background in AI at scale, high-performance computing, computer vision, and enterprise AI automation. My career combines deep technical expertise with AI leadership, bridging the space between research innovation and real-world deployment.
As Head of AI at ActiveEon (2017–2025), I led a team of Ph.D.-level engineers and designed large-scale AI infrastructures for industrial and research applications. My work includes multi-GPU orchestration, distributed training, edge/cloud hybrid AI pipelines, and real-time computer vision integration.
With a strong foundation in software engineering (since 2001) and AI research (since 2010), I bridge the gap between research innovation and enterprise-grade AI deployment. Experienced in end-to-end development: algorithm design, optimization, GPU acceleration, embedded deployment, and production-grade inference.
Core Expertise
- AI Architecture & Scale — HPC for AI | Distributed & Parallel Workflows | Multi-GPU/node scaling | Federated & hybrid-cloud systems
- MLOps & Automation — Experiment tracking | Continuous Training (CT) pipelines | CI/CD for ML | AI workflow orchestration (cloud, on-prem, HPC) | Monitoring, drift detection, and model governance
- Computer Vision & Image/Video Processing — Object detection, anomaly detection, motion analysis | Real-time video analytics & CV pipelines | Background subtraction, low-rank/sparse decomposition
- Embedded & Edge AI — Real-Time Inference (Jetson, RPi) | TensorRT optimization | DeepStream/GStreamer | Constrained hardware inference
- AutoML — Hyperparameter Optimization (HPO) | Neural Architecture Search (NAS)
- LLMs & GenAI — Fine-tuning, PEFT/LoRA, Quantization | FlashAttention, AutoAWQ | Deployment (Triton, TGI, vLLM)
- Stack — C++, Python, CUDA, AI SDKs, APIs, JupyterLab Integration
Leadership @ ActiveEon — Former Head of AI (2017–2025)
At ActiveEon, I architected and delivered large-scale AI solutions tailored for industrial and research applications. My responsibilities included:
- Led a team of researchers in AI/ML, GenAI, and Vision Systems
- Architected ProActive AI Orchestration (PAIO) for scalable, cloud-native, and on-prem AI workflows
- Deployed LLMs, deep learning, and AutoML pipelines across multi-node, multi-GPU, and hybrid cloud environments
- Delivered AI use-cases for clients such as Thales Alenia Space and SAFRAN
- Built custom SDKs and tools for dynamic workflow automation (e.g., ProActive Python SDK, ProActive Jupyter Kernel)
- Contributed to the ExtremeXP European research project by leading the implementation of a runtime for scheduling and executing complex analytics workflows on distributed infrastructure using ProActive AI Orchestration (PAIO). This includes integration for distributed AutoML, resource monitoring, and dynamic service orchestration via the ProActive Python SDK, enabling on-the-fly deployment of tools like TensorBoard and MLOps dashboards.
Academic Background & Research
Ph.D. in Computer Vision & Machine Learning – Université de La Rochelle, France
Specialized in:
- Low-rank & sparse matrix/tensor decomposition
- Subspace learning & optimization
- Multimodal and real-time video analytics
- Embedded computer vision (Jetson, Raspberry Pi, PandaBoard)
My academic research has led to 2800+ citations (h-index: 17) and contributions to peer-reviewed journals and top conferences (IEEE, Elsevier, Springer, CVIU, TIP, TNNLS, ICCV). I also actively contribute as a peer reviewer and open-source developer.
Open-Source & Contributions
🔗 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 in academia, industry, robotics, surveillance systems, and research labs worldwide.
Let's Collaborate
I'm open to opportunities in:
- AI architecture & engineering
- Computer Vision & embedded AI
- Distributed AI systems & HPC+AI
- Consulting, research collaborations, and advanced AI prototyping
If you are building AI systems that require performance, scalability, or strong engineering fundamentals, I'd be happy to discuss how I can help.