I am an AI Engineer with passion and hands-on experience in Advanced RAG Systems, Agentic AI, Machine Learning, forecasting, Regression models, Agent Orchestration via LangGraph, and Distributed Systems with Azure Logic Apps. Currently working as an AI Engineer at Endeavour in Haarlem, Netherlands, where I am working on a Conversational AI search engine.
I am passionate about complex ML models like ST-GNNs and enjoy designing solutions through innovative model architectures. In my free time I also read the newest research papers in the field.
- BSc in Artificial Intelligence - Vrije Universiteit Amsterdam, The Netherlands (2024)
- Focus on: Intelligent Systems, applied machine learning and software applications
- Advanced topics: Computational Intelligence, Machine learning, Text mining (NLP), Software Engineering, Knowledge representation, Logic, Machine Learning, Statistics
- Bachelor's in Management Engineering - Uninettuno University, Italy
- Mathematics, Physics, Business Administration, Statistics, Thermodynamics
- Advanced English Course - TM International School of English, Cambridge, UK
AI Engineer at Endeavour (2026) AI/Software Developer at Nedstar (July 2025 - February 2026) - Amsterdam, North-Holland, Netherlands
- Independently automated invoice processing for 6,500+ documents/year using AI, cutting manual effort by 70%
- Deployed intelligent document processing with custom ML models trained on 2 years of historical data, automating 95% of invoices
- Integrated RAG system with Business Central & Azure, implementing risk-based approval workflows with Blob Storage with consecutive real-time stream of changes via webhook, CosmosDB for state management and FastAPI for serving
Enterprise-scale document processing system automating 6,500+ invoices annually with 95% automation rate
Tech Stack: 
Key Achievements: 70% reduction in manual effort • 95% automation rate • Real-time processing
Impact: Processing 6,500+ documents/year with intelligent risk-based approval workflows
Technical Architecture
RAG Pipeline: Custom ML models trained on 2 years of historical invoice data Integration Layer: Business Central & Azure Blob Storage with real-time webhooks State Management: CosmosDB for persistent workflow state and audit trails API Layer: FastAPI for high-performance document processing endpoints Orchestration: LangGraph agents for intelligent workflow routing and validation
Key Innovations: Risk-based approval routing with ML-driven confidence scoring Real-time stream processing for immediate invoice status updates Custom OCR pipeline optimized for invoice layouts and formats Automated vendor master data reconciliation and validation
Multi-agent system for business process automation with distributed coordination
Tech Stack: 
Features: Multi-agent coordination • Persistent memory • Tool orchestration
Agents: Document Parser • Data Validator • Business Logic • Workflow Controller
Agent Architecture
Coordination Layer: State machines for complex workflow orchestration Memory Management: Redis-backed persistent conversation and context memory Tool Integration: 4 specialized tools for data processing and external system integration Monitoring: Real-time agent performance tracking and decision logging
Agent Specializations: Document Agent: Multi-format parsing, structure extraction, content validation Validation Agent: Business rule enforcement, data quality checks, compliance verification Integration Agent: ERP system connectivity, API orchestration, data synchronization Monitoring Agent: Performance tracking, anomaly detection, alert management
Advanced time series forecasting with hierarchical LSTM for complex pattern recognition
Tech Stack: 
Focus Areas: Hierarchical LSTM • Multi-variate forecasting • Spatial-temporal modeling
Research: Novel architectures for complex pattern recognition in time series data
Research & Innovation
Hierarchical LSTM Models: for capturing complex dependencies on three time levels. Forecasting Pipeline: End-to-end system for multi-horizon prediction tasks Model Architecture: Custom attention mechanisms for temporal and spatial relationships Evaluation Framework: Comprehensive benchmarking against traditional and modern methods
Technical Contributions: Novel graph construction methods for time series relationships Attention-based temporal modeling with memory mechanisms Multi-scale feature extraction for diverse forecasting horizons Production deployment patterns for real-time inference
Enterprise integration platform with Azure Logic Apps for seamless business process automation
Tech Stack: 
Solutions: Workflow automation • System integration • Event-driven architectures
Integrations: ERP systems • CRM platforms • Document management • API orchestration
Integration Architecture
Workflow Engine: Azure Logic Apps for complex business process orchestration Event Processing: Real-time event streaming and processing pipelines API Management: Centralized API gateway with authentication and rate limiting Data Transformation: ETL pipelines for data harmonization across systems
Key Implementations: Multi-system data synchronization with conflict resolution Automated approval workflows with escalation rules Real-time monitoring and alerting for business processes Scalable integration patterns for enterprise applications
