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, and Deep Learning
Expert in AI at Scale, HPC+AI, MLOps, Computer Vision, Generative AI, and LLM Optimization.
👉 Currently open to senior AI leadership and consulting opportunities.
I am an AI Architect and Researcher with deep expertise in AI at Scale, High-Performance Computing (HPC),Generative AI, Computer Vision, and Enterprise AI Automation. As Former Head of AI at ActiveEon (2017–2025), I led a team of Ph.D.-level researchers in developing cutting-edge AI solutions for industries such as space, energy, and aerospace.
With a strong foundation in software engineering, AI research, and distributed systems, I bridge the gap between research innovation and enterprise-grade AI deployment. My work focuses on MLOps, multi-GPU/cluster orchestration, and real-time computer vision for edge and cloud systems.
Core Expertise
- AI at Scale | HPC+AI | Distributed & Parallel AI Workflows
- MLOps | GenAI & LLM Optimization | Model Monitoring & Drift Detection
- Generative AI | LLM Fine-Tuning & Deployment | AI-Orchestrated Pipelines
- Computer Vision | Video Analytics | Object Detection & Anomaly Detection
- Embedded & Edge AI | Real-time AI on NVIDIA Jetson, Raspberry Pi
- AutoML | Hyperparameter Optimization | Neural Architecture Search
- Software Engineering | C++, Python | AI SDKs, APIs, and 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 and Sparse Matrix Decomposition, Tensor Optimization, and Subspace Learning
- Multimodal Video Analytics, Real-Time Detection, and Edge AI
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
- BGSLibrary – Background Subtraction (C++)
- LRSLibrary – Low-Rank Sparse Decomposition (MATLAB)
- MTT, OSTD, IMTSL – Tensor Tools for Vision & Subspace Learning
- VDTC – Vehicle Detection, Tracking, and Counting
🔗 GitHub: github.com/andrewssobral
Let's Collaborate
I'm open to opportunities in:
- AI Research & Consulting
- Distributed AI Systems & Enterprise Automation
- Vision Systems & Edge AI Deployment
- Generative AI & LLM Optimization