AI Engineer at UniKey, designing agentic AI systems and custom machine learning for healthcare and drug development. MSc in AI & Data Engineering from the University of Pisa, with research in generative and geometric deep learning for medicine and genetics.
MSc in AI & Data Engineering
University of Pisa
I'm an Artificial Intelligence Engineer at UniKey, where I design agentic AI systems and custom machine learning solutions for health, healthcare and drug development. Previously I built high-performance, real-time machine vision software in C/C++ and CUDA for the defense sector at Akkodis, and backend microservices for large-scale tolling systems at RJC Soft.
I hold a Master's degree in Artificial Intelligence and Data Engineering from the University of Pisa (grade 103/110), following a Bachelor's in Computer Engineering. My research centres on generative and geometric deep learning for medical and genetic applications, reflected in my theses on Aptamer Predictive Triage and RNA secondary structure prediction.
Specialized in generative AI, LLM integration and agentic systems (LangChain, MCP), alongside deep learning, neural networks and predictive modeling for real-world products.
Expert in big data processing with Apache Spark, Kubernetes, ElasticSearch and ETL pipelines, building scalable, high-availability data architectures.
Applying AI to healthcare and drug development: agentic ML pipelines, aptamer design, DNA/RNA analysis, Gene Editing and Personalized Medicine, with a focus on longevity and bio-hacking.
I build autonomous, tool-using AI agents through LLM orchestration, retrieval-augmented generation (RAG) and custom vector embeddings with LangChain and the Model Context Protocol (MCP) — engineering multi-step reasoning, function calling and safety guardrails so agents act reliably on real tasks.
I design and train deep neural architectures — transformers, convolutional and graph neural networks — for pattern recognition, sequence modeling, time-series forecasting and predictive analytics, owning the full loop of data preparation, training, hyperparameter tuning and evaluation in PyTorch.
I engineer real-time computer-vision pipelines and low-latency critical software, optimizing inference in C/C++ and CUDA with GPU kernel tuning and memory-aware design for mission-critical, high-reliability, high-throughput environments.
I build scalable, distributed data pipelines with Apache Spark, ElasticSearch and ETL workflows orchestrated on Kubernetes, designing high-availability architectures for large-scale batch and streaming processing.
I ship containerized microservices with Docker, Kubernetes and Spring Boot, wiring up CI/CD, observability and infrastructure-as-code across cloud and self-hosted deployments.
I apply generative and geometric deep learning to drug development, aptamer design, RNA secondary-structure prediction and personalized medicine — building structure-aware models for bioinformatics and computational biology.
Explore AI for Healthcare & Medicine →You can download my CV to get a more detailed overview of my skills and experience.