Nessim B.

- Architected enterprise-scale SPA using Domain-Driven Design with 40+ reusable Vue 3 components across several feature modules, implementing Factory and Repository patterns for clean separation between domain entities, business logic, and API integration layers.

Built comprehensive profile management system with 14 pages featuring inline editing, form validation framework with abstract base classes, and custom notification service with auto-dismiss flashbag component

- Engineered full-stack integration using Axios repositories with Vite proxy configuration, Docker containerized development environment with hot-reload, Vue Router authentication guards, and Vuex state management for seamless backend communication

- Implemented modern UX features including Vue i18n internationalization (EN/FR), custom SCSS design system with 4-color palette and CSS custom properties, Bootstrap-Vue-Next component auto-import, FontAwesome integration, masonry grid layouts, and responsive mobile-first design

- Architected and developed a Flask-based REST API using Clean Architecture (Hexagonal) principles with strict layer separation (Domain, Application, Infrastructure), implementing Repository, Factory, and Strategy patterns with pysman dependency injection to ensure maintainable, testable code across 13+ domain entities

- Built multi-format PDF resume generation system using fpdf2 with distinct strategies

- Engineered data persistence layer using SQLAlchemy Core (non-ORM) with PostgreSQL, implementing repository interfaces, managing complex entity relationships through factories, and maintaining schema versioning with Alembic migrations

- Implemented secure authentication system with Flask-Login and Flask-Bcrypt, designed RESTful endpoints with camelCase/snake_case transformation, configured CORS for external integrations.

- Architected and developed an AI-powered job matching REST API using Flask and Clean Architecture principles to aggregate job postings from a variety of platforms, SDKs and Perplexity AI integration

- Built intelligent semantic matching system using pgvector (PostgreSQL 17) with multidimensional embeddings from Sentence-Transformers, computing cosine similarity with NumPy to match job postings against user profiles with 70% accuracy threshold

- Implemented automated job aggregation pipeline using APScheduler for daily cron jobs, computing embeddings in batch operations for performance optimization, with comprehensive error handling and logging to process thousands of job postings daily and link only semantically relevant matches to user search histories

Building PySMan, a standalone Python service manager with autowiring capabilities, designed to be embedded in a wide range of applications (for example, desktop tools built with Tkinter or web services built with Flask).

Pushing the module to PyPI so that it can be used as a package in any project