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Industry fields of expertise
Benjamin in a few words
Data scientist turned DevOps Engineer equally versed in deployment and maintaining of applications as in their development.
My languages of choice are Rust for systems programming, Golang for web backend, and Python for science.
Expert in Linux (Kernel API, Bash, SysAdmin) and fluent in Microsoft/Windows environment (C#, Powershell, DSC).
Research and Deep Learning experience as research student at Toulon University and NASA Jet Propulsion Laboratory for comet detection on astronomical infrared multispectral images and then at IBM Watson (1 year) for detection of infant disease on electroencephalograms and handwritten medical documents text extraction, both in Linux, Python and Tensorflow/Keras environment.
Subsequent experience of 3 years as DevOps and Systems Engineer at IAM editor company Netwrix Usercube in an Azure/Microsoft environment, where I was the main force behind the full restructuration of the infrastructure via Terraform to be fully as code and checked into source control, and finally the decomposition of the monolithic multi-tenant product into a micro-service architecture with Kubernetes on AKS and C#/.NET development of the backend to adapt the parts into the new architecture. (CI/CD on Azure on the side and support & coverage for the system administrator for critical tasks and during heavy load)
I personally use Python, Go, Rust, Kubernetes, Docker, Linux, Bash and Ansible regularly, as I maintain my own Kubernetes cluster serving private web services & personal web apps.
Experience
- Netwrix CorporationDevOps & Production EngineerDIGITAL & ITJuly 2022 - Today (2 years and 12 months)London, UK
DevOps for Production:
- Production infrastructure provisioning with Terraform modules- Centralization & separation of SaaS configuration from infrastructure with ad-hoc resource for configuration fetching- Automatic client metrics aggregation to App InsightsSaaS Administration:
- Multi-tenant production infrastructure configuration with source control- N3 support: critical production and preproduction infrastructure issues resolution- Global monitoring of infrastructure with Grafana and App Insights alerts - UsercubeDevOps and Systems Engineer in IAM/IGADIGITAL & ITJanuary 2021 - Today (4 years and 6 months)Marseille, France
Development of Usercube's SaaS Environment:
- Fragmentation of the main software into a full Micro-Service approach:
- Deep understanding of the underlying layers of the program in order to determine the atomic parts of it
- Splitting of the parts and transformation of each into a micro-service via a Docker container to hold the service
- Constitution of Kubernetes manifests out of each Usercube Micro-Service
- POC: Make the Usercube software fully functional in its micro-service form
- Architecture of a Production micro-service Infrastructure via Helm charts and provisioning of cluster Azure resources with Terraform
[Technologies: Kubernetes, Docker, Terraform, Azure, C#, .NET]Transformation of QA environment into a fully IaC and source-controlled declarative configuration:
- Re-creation of Windows Server VMs and Linux Containers via provisioning with Terraform and configuration management with Ansible
- Using Cloud Virtual Network for local communications between the VMs and containers while ensuring full protection from the public web.
- Configuration of Active Directory Domain Controllers and LDAP servers. [Technologies: Terraform, Docker, Ansible, DSC, Powershell, Bash, Azure CLI, Azure, Active Directory, Azure AD, LDAP ] # Full-Stack Development:
- Development of Features and resolution of Bugs in the Solution
- QA and Code Review of Work Items
- CI/CD with Azure Pipelines [Technologies: C#, .NET, ASP.NET, Node.js, React.js, Typescript, Azure Devops]
- IBMWatson AI Technical ConsultantRESEARCHMarch 2019 - December 2019 (10 months)Paris, France
- Extensive Computer Vision state of the art paper for internal use
Research project regarding automation of trauma (Hypoxic Ischemic Encephalopathy) detection on infants with Electroencephalogram (EEG) analysis :
- Analyse EEG behaviors, limitations and characteristics of affected patients
- Different signal processing tools : power spectral density on FFT of EEG, Morlet wavelets decomposition, Notch filters.
- Clustering on labeled data for binary classification
Text classification for different mail buckets :
- NLP multilayer perceptron model with TfIdf vectorization
- Hyperparameter fine-tuning on NLP text classifier in production
- Continuous Integration with regular additional feeding
- R&D for text classification and text extraction on unstructured data :
Object detection pipeline for handwritten text with on images with modified Faster RCNN model for Scene Text Recognition
* CRNN model with CTC loss for Handwritten Text Recognition
Recommendations
Education
- Master of Engineering422023Master of Engineering - MEng, Computer Science
- Master of EngineeringEcole Centrale de Marseille2019Master of Engineering - MEng, Generalist
Certifications