Hamza Ben Jannet

■ Observability & Monitoring: Implementation of dashboards and log analysis via the ELK stack (Elasticsearch, Kibana).

■ Multi-Framework Front-end: Development of complex and high-performance UI interfaces in ReactJS.

■ Maintenance and evolution of administration back-offices under Angular.

■ CI/CD & DevOps: Automation of build, test, and deployment pipelines via GitHub Actions and containerization of services with Docker.

■ Documentation & Craftsmanship: Writing in-depth technical documentation on Confluence and evangelizing best practices through code reviews (Pull Requests) and mentoring.

■ Agile & Product Leadership: Facilitating daily rituals (Daily) and participating in PI Plannings for sprint synchronization.

■ Technical onboarding of new developers and mentoring on architecture patterns.

■ Close collaboration with Product Owners for refining business needs and demonstrating new features (Sprint Demos).

■ Technical support and collaboration with QA teams for qualifying releases before production.

PROJECT: Design, creation, and improvement of a dynamic mobile application with Node.js data synchronization back-end servers for IBM Maximo professionals

Complete orchestration of the product lifecycle "MAXapps", from architectural redesign (Cordova to React Native/Expo migration) to integration with end customers. As a central pivot between Business Units, DevOps teams, and clients, I led the development of a dynamic mobile solution for industrial maintenance. The mission includes a strong R&D dimension for IoT integration, the design of strategic POCs for real-time geolocation, and the implementation of a technical and functional documentation standard to ensure the longevity of solutions deployed in critical environments.

Listening to AI

Description: The objective of this mission is the technical direction and development of an innovative SaaS platform dedicated to Social Media Monitoring and intelligent investigation management. The project consists of transforming massive volumes of unstructured data into actionable insights through the integration of AI models (LLM). As CTO, I oversee the multi-stack architecture (Python/Node.js/PHP), hybrid infrastructure (Cloud/On-premise), and operational excellence of the development teams, while ensuring the scalability of natural language processing (NLP) solutions.

PROJECT: Data Analysis & AI-Augmented Social Listening Platform

Description: The objective of this mission is to lead the technical vision and development of an innovative SaaS platform dedicated to Social Media Monitoring and intelligent investigation management. The project consists of transforming massive volumes of unstructured data into actionable insights through the integration of AI models (LLM). As CTO, I oversee the multi-stack architecture (Python/Node.js/PHP), hybrid infrastructure (Cloud/On-premise), and operational excellence of the development teams, while ensuring the scalability of natural language processing (NLP) solutions.

SCOPE OF WORK:

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Development and Design: Design of a modular and scalable architecture integrating microservices in Python (FastAPI, Django) for AI and Node.js for real-time. Implementation of an artificial intelligence layer based on GPT models via Ollama, enabling sentiment analysis, trend extraction, and automatic summarization of customer feedback. On the Frontend side, leading the redesign of analytical dashboards in Vue.js and React, using reusable component libraries and Redux-toolkit for complex state management. The infrastructure is designed to support intensive ML workloads on AWS, Azure, and GCP, with automation of business workflows via N8N and generalized containerization under Docker.

■ AI & Large Language Models (LLM): Integration and optimization of GPT models locally via Ollama for natural language processing, semantic analysis, and intelligent interpretation of surveys and social media.

■ Predictive Analysis & ML: Application of Machine Learning techniques for identifying emerging trends and implementing predictive analyses on large textual datasets.