Julien Raynaud

Project description:

Quarkslab is a cybersecurity company founded over ten years ago. The company combines offensive and defensive approaches to protect applications and data, helping businesses and government organizations adopt a proactive security posture.

QFlow is one of Quarkslab’s flagship solutions. It is an advanced malware detection and analysis platform designed to be scalable and cloud-friendly. QFlow integrates various tools and techniques to automate routine tasks related to threat detection and analysis, allowing security teams to focus on targeted actions. It provides comprehensive threat analysis, seamless integration into existing infrastructure, and ensures the confidentiality of sensitive data through on-premises deployment or trusted SaaS options.

Team: 8

Achievements:

• Maintenance of the platform and probes.

• Development of a probe to analyze APK files.

• Integration of Keycloak as a Service Provider to replace Ory in order to support the SAML protocol.

• Refactoring of the REST API in FastAPI.

• Automation of probe updates with CI/CD.

• Development of a REDIS STREAM adapter in Python to manage events between different platform components.

• Development of a load tester based on Molotov.

• Development of a Python component to generate daily, weekly, and monthly reports (JSON and CSV) related to threat analysis, also sent to Grafana.

• Writing of technical documentation.

Technical environment / functional / methods:

• Programmation / Framework: Python (FastAPI, Hug), Bash Scripting

• Databases / Data Storage: Postgresql, Redis, Minio

• Message Queuing / Data Streaming: Redis (pub/sub, Redis stream)

• Development Tools / DevOps: VS Code, Git, Gitllab, Docker, Kubernetes, Linux, Nexus, Keycloak, Rancher

• API Testing: Postman, HTTPie, Pytest

• Methods: Agile (Kanban), TDD

Project description:

Jellysmack is a company specializing in helping content creators grow their communities and maximize their revenue across various social platforms. It offers content amplification solutions and programs dedicated to creators.

JellyPulse is Jellysmack’s exclusive technology, encompassing content management and audience analytics tools. It analyzes creators' existing content across different platforms, tracks engagement and reactions, and provides recommendations to improve existing videos and produce new content tailored for platforms like Facebook and YouTube.

Team: 8/12

Achievements:

• Maintenance of API (FastAPI) and development of new ones.

• Replacement of the Pipedrive CRM with Salesforce.

• Development of a distributed architecture on AWS around Salesforce, JellyPulse, and other internal Jellysmack products.

• Development of connectors to retrieve creator data from Tubular and other third-party applications.

• Development (Terraform) of all new AWS components for JellyPulse (Salesforce connector, ECR for APIs, Lambdas, SNS, SQS, etc.).

• Development of a GraphQL API in Python.

• Writing of technical documentation (GitLab, Notion).

Technical environment / functional / methods:

• Programmation / Framework : Python (FastAPI), Javascript, Bash Scripting

• Databases / Data Storage : Postgresql, Mysql (RDS), Elasticsearch, Redis, S3 (AWS)

• Message Queuing / Data Streaming : Redis (pub/sub), SNS, SQS, KinesisStream, EventBridge

• Development Tools / DevOps : VS Code, Git, Gitllab, Docker, Kubernetes, Terraform, Linux, Nexus, Jfrog, AWS (EC2, ECR, EKS, IAM, Lambda), CloudWatch, Datadog

• API Testing : Postman, Pytest

• Methods : Agile (Scrum 2 weeks sprint / Kanban), TDD, DDD

Project description :

AI-Bettana is an artificial intelligence designed to enhance the customer experience on e-commerce platforms in the fashion sector. It offers an image consulting chatbot based on each customer's morphology, providing personalized recommendations for ready-to-wear clothing.

Through a complete morphological scan, AI-Bettana analyzes characteristics such as height, body shape, and the color of eyes, hair, and skin to suggest clothing suitable for each individual.

This approach aims to personalize the online shopping experience by replicating the experience of a physical store. By offering items that match the customer’s morphology and style, AI-Bettana helps e-commerce retailers simplify and speed up the sales process, while reducing returns related to sizing or fit issues.

Team : 5 persons

Achievements:

• Management of technical debt with the organization of the API refactor, database structure, and distributed architecture on AWS.

• Improvement of the size recommendation algorithm.

• Deployment of the solution in production for the first clients.

Technical environment / functional / methods:

• Programmation / Framework: Python (Flask), Bash Scripting

• Databases / Data Storage: Mysql, Elasticsearch, Redis

• Message Queuing / Data Streaming: RabbitMQ

• Development Tools / DevOps: VS Code, Git, Gitllab, Docker, Kubernetes, Linux, Nexus, AWS (EC2, AM), CloudWatch

• API Testing: Postman, Pytest

• Methods: Agile (Scrum 2 weeks sprint), TDD