*Using DeepSeek's API and running DeepSeek's open-weight models locally are fundamentally different things.*

When using an API, sensitive data may be transmitted to external servers. That's a legitimate security concern.

But when model weights are deployed on government-controlled infrastructure, especially in an air-gapped environment with no internet access, there is simply no network path for the model to transmit data back to China or anywhere else.

Of course, software supply-chain security matters. But those risks should be evaluated independently, regardless of whether a model comes from China, the US, or Europe.

*If this ban also covers locally hosted, isolated deployments, it is conflating the nationality of a model's developer with an actual cybersecurity vulnerability.*

Security policies should be based on concrete threat models and technical evidence, not on where a model was trained.

Otherwise, we're not really talking about cybersecurity anymore. We're talking about geopolitics disguised as cybersecurity.

Remote: Yes (preferred) or Hybrid (from Paris)

Willing to relocate: No

Résumé/CV: https://andrewssobral.pages.dev

GitHub Profile: https://github.com/andrewssobral

LinkedIn: https://www.linkedin.com/in/andrewssobral

Email: andrewssobral@gmail.com

Summary: AI Systems and Platform Architect 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.

Technologies: Python, C/C++, PyTorch, OpenCV, scikit-learn, CUDA, FastAPI, Docker, Kubernetes, MLflow, Hugging Face, REST APIs, Cloud (AWS, Azure, GCP), MLOps, CI/CD, Generative AI, LLMs, HPC (Slurm), Distributed Systems, Dask, Ray

Summary: AI Systems Architect with 20+ years in software engineering and 10+ years specializing in AI, ML, and Computer Vision. Former Head of AI at ActiveEon, with a Ph.D. focused on Computer Vision and Machine Learning. Expert in scalable AI systems, MLOps, distributed computing, and fine-tuning of large language and vision models. Passionate about building practical, production-ready AI solutions that bridge research and deployment. Open to roles involving AI architecture, applied research, or leadership in AI strategy and innovation.

Technologies: Python, C/C++, PyTorch, OpenCV, scikit-learn, CUDA, FastAPI, Docker, Kubernetes, MLflow, Hugging Face, REST APIs, Cloud (AWS, Azure, GCP), MLOps, CI/CD, Generative AI, LLMs, HPC (Slurm), Distributed Systems, Dask, Ray

Résumé/CV: https://andrewssobral.pages.dev

GitHub Profile: https://github.com/andrewssobral

LinkedIn: https://www.linkedin.com/in/andrewssobral

Email: andrewssobral@gmail.com

Summary:

AI Architect & Researcher with 20+ years in software engineering and 10+ years specializing in AI, ML, and Computer Vision. Former Head of AI at ActiveEon, with a Ph.D. focused on Computer Vision and Machine Learning. Expert in scalable AI systems, MLOps, distributed computing, and fine-tuning of large language and vision models. Passionate about building practical, production-ready AI solutions that bridge research and deployment. Open to roles involving AI architecture, applied research, or leadership in AI strategy and innovation.

Technologies:

Python, C/C++, PyTorch, OpenCV, scikit-learn, CUDA, FastAPI, Docker, Kubernetes, MLflow, Hugging Face, REST APIs, Cloud (AWS, Azure, GCP), MLOps, CI/CD, Generative AI, LLMs, HPC (Slurm), Distributed Systems, Dask, Ray

Willing to relocate: case-by-case

Résumé/CV: https://andrewssobral.pages.dev

GitHub Profile: https://github.com/andrewssobral

LinkedIn: https://www.linkedin.com/in/andrewssobral

Email: andrewssobral@gmail.com

Summary:

AI Architect & Researcher with 20+ years in software engineering and 10+ years specializing in AI, ML, and Computer Vision. Former Head of AI at ActiveEon, with a Ph.D. focused on Computer Vision and Machine Learning. Expert in scalable AI systems, MLOps, distributed computing, and fine-tuning of large language and vision models. Passionate about building practical, production-ready AI solutions that bridge research and deployment. Open to roles involving AI architecture, applied research, or leadership in AI strategy and innovation — either full-time or consulting.

Technologies:

Python, C/C++, PyTorch, OpenCV, scikit-learn, CUDA, FastAPI, Docker, Kubernetes, MLflow, Hugging Face, REST APIs, Cloud (AWS, Azure, GCP), MLOps, CI/CD, Generative AI, LLMs, HPC (Slurm), Distributed Systems, Dask, Ray

Remote: Yes (preferred) or Hybrid

Willing to relocate: case-by-case

Résumé/CV: https://andrewssobral.pages.dev

GitHub Profile: https://github.com/andrewssobral

LinkedIn: https://www.linkedin.com/in/andrewssobral

Email: andrewssobral@gmail.com

Summary:

AI Architect & Researcher with 20+ years in software engineering and 10+ years specializing in AI, ML, and Computer Vision. Former Head of AI at ActiveEon, with a Ph.D. focused on Computer Vision and Machine Learning. Expert in scalable AI systems, MLOps, distributed computing, and fine-tuning of large language and vision models. Passionate about building practical, production-ready AI solutions that bridge research and deployment. Open to roles involving AI architecture, applied research, or leadership in AI strategy and innovation — either full-time or consulting.

Technologies:

Python, C/C++, PyTorch, OpenCV, scikit-learn, CUDA, FastAPI, Docker, Kubernetes, MLflow, Hugging Face, REST APIs, Cloud (AWS, Azure, GCP), MLOps, CI/CD, Generative AI, LLMs, HPC (Slurm), Distributed Systems, Dask, Ray

Remote: Yes (preferred)

Willing to relocate: case-by-case

Résumé/CV: https://andrewssobral.pages.dev

Email: andrewssobral@gmail.com

Summary:

AI Architect & Researcher with 20+ years in software engineering and 10+ years specializing in AI, ML, and Computer Vision. Former Head of AI at ActiveEon, with a Ph.D. focused on Computer Vision and Machine Learning. Expert in scalable AI systems, MLOps, distributed computing, and fine-tuning of large language and vision models. Passionate about building practical, production-ready AI solutions that bridge research and deployment. Open to roles involving AI architecture, applied research, or leadership in AI strategy and innovation — either full-time or consulting.

Technologies:

Python, C/C++, PyTorch, TensorFlow, OpenCV, scikit-learn, CUDA, ONNX, FastAPI, Docker, Kubernetes, MLflow, Hugging Face, REST APIs, Cloud (AWS, Azure, GCP), MLOps, Generative AI, LLMs, HPC (Slurm), Distributed Systems, Dask, Ray