Guillaume Passinay

Design and complete architecture of a SaaS platform dedicated to psychologists and clinical experience. Real traction: more than 600 users.

ACHIEVEMENTS

› Product vision, technological choices, and environment structuring

› Building a specialized AI for psychologists (clinical assistant, structured analysis, help with therapeutic writing and

reflection)

› Development of complementary AI agents: data scraping, business automations, multi-source orchestration, RAG

› Full-stack development (backend, frontend, API integrations) and business-use-centric UI/UX design

› Automated creation of avatar videos via AI

› Cloud deployment on AWS (infrastructure, CI/CD, scalability, monitoring), production launch, and industrialization

› Securing health data and HDS compliance

› Technical documentation, structuring best practices, and knowledge transfer

Environment: Python, FastAPI, Next.js, Flutter

• · LLM (Claude, Gemini), RAG, AWS Bedrock, Hugging Face, vLLM

◦ · code agents (Claude Code, Codex)

• · AWS (Lambda, S3, CloudFront, Aurora, Bedrock), GCP

◦ · REST API, Docker

◦ · SaaS, AI / NLP, GDPR, HDS,

Improvement of data quality and analysis of MyCanal content consumption within the Data department.

ACHIEVEMENTS

› Development of a data quality measurement and evaluation tool to improve the quality of data received and produced within the Data department

› Development of third-party applications for analyzing MyCanal content consumption

› Industrialization of data pipelines on AWS (S3, Lambda), orchestrated with Airflow

› Data modeling and exploitation in Snowflake for analytics needs

› Implementation of continuous integration (GitLab CI) and infrastructure as code (Terraform)

Environment: Scala, Spark, Python, AWS (S3, Lambda), Airflow, Snowflake, GitLab CI, Terraform, Data Quality, Analytics,

ACHIEVEMENTS

› Development of data transformation pipelines from various source systems (batch and real-time)

› Industrialization of projects up to production launch

› Development of integration tests and optimization of processing performance

› Implementation of a performance testing tool (load testing according to target objectives)

› Incident analysis and resolution (job monitoring, log analysis)

› Contribution to documentation, Agile Scrum methodology

Environment: Cloudera, Scala, Spark Streaming, Kafka, Hadoop, Mesos / Yarn, Kerberos, Oozie, Git, Maven, Spring Boot,