SAMI BEN HASSINE
Tech Lead with over 6 years of experience Expert in Python and JavaScript, with deep expertise in FastAPI, Flask, Vue.js, Nuxt.js, Next.js and React. Specialized in designing scalable and high-performance systems, leading cross-functional teams, and developing software applications with DevOps workflows and AWS Cloud environment.
Technical Skills
Comprehensive expertise from design to production
Frontend Development
Frontend development with Next.js (React) or Nuxt.js (Vue.js), leveraging recognized UI libraries such as shadcn/ui.
Backend Microservices
Backend microservices development using FastAPI or Flask frameworks, with implementation of asynchronous communication via messaging systems such as RabbitMQ, Apache Kafka, Redis (native), Celery, or cloud services like AWS SQS.
Cloud Infrastructure
Cloud infrastructure setup using Terraform: IAM roles and permissions management, networking, databases, storage, security, and filtering rules.
Containerization
Microservices containerization using Docker.
Kubernetes Orchestration
Deployment and orchestration of Kubernetes clusters, with manifest definitions (Deployments, Services, Ingress, ConfigMaps, Secrets, etc.).
CI/CD & Automation
Implementation of CI/CD pipelines to automate deployments across different environments (development, staging, production).
Monitoring & Observability
System and service monitoring, supervision, and observability.
Soft Skills
Leadership & Team Management
Mentoring and managing teams to drive efficiency and innovation.
Project Management
Agile methodology (Scrum, Kanban), roadmap planning, and execution.
Communication & Collaboration
Bridging technical and non-technical teams for seamless development cycles.
Problem-Solving
Analyzing and improving application performance with scalable solutions.
Initiative & Innovation
Leading the development of cutting-edge SaaS products and AI-driven applications.
System Design
Designing scalable, maintainable, and efficient architectures that ensure performance and reliability.
Professional Experience
AI Engineer
Legal Document Editor & Multi-Format Workspace (Berlioz)
Led the product surface for litigation-grade document work: massive file trees, DOCX/XLSX/PDF/image tooling optimized for review, agentic AI on legal corpora, snapshotting, change tracking, and certified commit flows for traceability.
Developed core features:
- ▸High-scale legal file tree with lazy loading, fuzzy search, batch actions, and per-matter navigation state
- ▸Multi-format editors and viewers (TXT, DOCX, XLSX, PDF, images) with annotations and in-editor AI assistance
- ▸Agentic workflows for mass analysis, semantic legal search, and cost-aware LLM orchestration with RAG
- ▸Continuous snapshotting with hybrid sync, granular history, and conflict handling for long review sessions
- ▸Commit and change-tracker UX: integrity fingerprints, async bulk validation, and pre-commit diff visibility
Tech Stack:
AI Engineer
Development of an LLM-Powered Legal Application
Designed system architecture — defined database requirements, estimated infrastructure costs, planned horizontal and vertical scaling, selected cloud services, and ensured security & reliability.
Developed core features:
- ▸Document-based discussion interface for uploaded files
- ▸Semantic search engine for legal text similarity
- ▸Project-data chatbot assistant
- ▸File management system (upload, storage, retrieval)
- ▸AI-driven and real time monitoring dashboard with AI code review and fix solutions
Technical Leadership:
- ▸Cross-functional team leadership: led strategic alignment meetings and facilitated brainstorming sessions to maintain cohesion and shared vision
- ▸Feedback optimization: organized work in small iterative steps to accelerate learning and enable early problem detection
- ▸Team enablement: promoted collaborative practices (pair programming, code reviews) and developed team self-organization to maximize collective efficiency
- ▸Product vision: defined and communicated clear objectives, prioritized based on business value, and aligned with non-technical stakeholders
DevOps & Cloud Infrastructure:
- ▸Deployed scalable infrastructure on AWS EKS (Kubernetes) with horizontal and vertical scaling planning
- ▸Implemented CI/CD pipelines using AWS CodePipeline (connected to GitHub via AWS Connector) and CodeBuild for continuous integration and deployment
- ▸Managed Docker containers (ECR), Kubernetes orchestration, and infrastructure monitoring to ensure high availability and performance
- ▸Leveraged AWS Lambda serverless functions for specific on-demand processing tasks, optimizing application costs and scalability
Tech Stack:
Frontend Engineer / Frontend Tech Lead
E-commerce Data Analytics App
Key Responsibilities:
- ▸Frontend Tech Lead — Led frontend developers and collaborated with backend, data science, and DevOps teams
- ▸High-Performance UI/UX: Built intuitive interfaces with complex dashboards and data visualizations using Ant Design
- ▸Integrated LLM (Azure OpenAI) chatbots to explain pricing formulas and guide users
- ▸Performance optimizations: Memoization, lazy-loading, virtual scroll
Tech Stack:
Full Stack Engineer
Web Analytics Platform (User Behavior Tracking)
Built a highly scalable web analytics solution designed to handle massive traffic (millions of events/day) with distributed, high-availability architecture. Provides more relevant insights than Google Analytics through actual click analysis, demographic segmentation, and device analysis.
DevOps & Cloud Infrastructure:
- ▸Distributed architecture with multi-regional CDN (North America, Europe, Asia) and LRU eviction policy for cache optimization
- ▸Database scaling strategy: Read/Write separation (Leader/Followers), geographic sharding by user region, and multi-region replication
- ▸Kubernetes deployment with automatic horizontal auto-scaling, load balancers for multi-server routing, and high-availability infrastructure monitoring
Key Contributions:
- ▸Frontend: Developed analytics dashboard with heatmaps, session recordings, and user journey visualizations
- ▸Backend: Created JavaScript injection script for automated user data collection and data pipeline with Celery workers
- ▸Advanced Insights: Behavioral analysis based on actual clicks, demographic segmentation (age, gender, location), and granular device analysis (desktop, mobile, OS, browser)
Tech Stack:
Full Stack Engineer
E-Commerce Analytics Platform
Developed core features:
- ▸Frontend: Analytics dashboards with Chart.js and D3.js for purchase trends and customer journeys
- ▸Backend: APIs for order/product management, Python (Flask) scripts for traffic data processing
- ▸CRM Automation: Trigger-based emails (cart recovery, order follow-up)
- ▸Google Shopping Integration and A/B Testing Tools
- ▸DevOps: Containerized with Docker, deployed on Kubernetes (OVH Cloud), CI/CD pipelines
Tech Stack:
Personal Projects
Berlioz Monitoring
Real-time monitoring suite with Next.js dashboard, FastAPI backend, Celery/Redis pipeline, WebSocket toasts, and AI-driven brief analyses.
Key Features:
- ▸Unified error ingestion via Celery/Redis with structured payloads and UUID error ids
- ▸FastAPI microservice with encrypted-session auth for super-admins
- ▸Persistent logs API with pagination, text search, and advanced filters
Tech Stack:
ClarityOS
All-in-one productivity workspace with kanban boards, rich text & canvas notes, calendar, habit tracking, analytics, and AI assistant powered by Gemini.
Key Features:
- ▸Real-time collaboration with Convex backend for instant synchronization across devices
- ▸Dual-mode note system: Rich text editor (TipTap) and freehand canvas drawing
- ▸Advanced kanban boards with drag-and-drop, subtasks, comments, and assignments
Tech Stack:
Resumind
AI-powered resume analyzer with ATS scoring, structured feedback by category, and actionable recommendations powered by Claude 3.7 Sonnet.
Key Features:
- ▸Client-side architecture using Puter.js: no backend, no server costs, data stays private
- ▸Advanced ATS scoring with detailed breakdown and improvement suggestions
- ▸AI analysis powered by Claude 3.7 Sonnet for professional feedback
Tech Stack:
AI Short Video Generator
Multi-step AI pipeline that transforms topics into ready-to-edit YouTube Shorts with auto-generated scripts, voice-over, stylized imagery, and captions.
Key Features:
- ▸End-to-end automation: topic → script → voice-over → captions → imagery → video preview
- ▸Multi-AI pipeline orchestrated by Inngest with retry semantics and error handling
- ▸ElevenLabs TTS with live voice catalog preview and multilingual support
Tech Stack:
Education
Master's in Computer Science
Bachelor's in Computer Science
Baccalaureate in Experimental Sciences
Contact
Languages
French
Advanced Intermediate (C1)
English
Advanced Intermediate (B2)