Senior AI Engineer · Data Scientist
Available for B2B Consulting, Contract Roles, and Freelance Projects.
I partner with teams in industrial automation, retail, and consultancy to design, build, and deploy AI systems that actually ship — from LangGraph agents and GraphRAG with Neo4j to predictive maintenance pipelines and MLOps on Azure & AWS.
Trusted by industry leaders
02 / Tech Stack
Opinionated tooling across Python, PyTorch, and Neo4j — optimised for fast iteration, observability, and safe deployment on Azure & AWS.
Core Stack
Built around a lean, battle-tested toolchain for applied ML and agents.
Python
Core language for orchestration, data work, and legacy migrations.
PyTorch
Fine-tuning, custom heads, and experimentation when off-the-shelf falls short.
Neo4j
Graph-native storage for GraphRAG and complex reasoning over industrial processes.
Focus
Where I spend most of my time.
Retrieval-Augmented Generation
Document ingestion, hybrid search, evaluation, and guardrails for reliable answers.
AI Agents
LangGraph-based multi-agent systems orchestrating tools, workflows, and memory.
MLOps
CI/CD, monitoring, and rollback-safe deployments on Azure & AWS (certified).
Stats
Years of experience
4+
Projects delivered
15+
Ecosystem
A tight ecosystem to keep latency low, costs predictable, and iteration loops short.
LLM Providers
OpenAI, Azure AI
Vector / Search
Hybrid search, GraphRAG
Infra
Azure, AWS, Docker, K8s
Monitoring
Metrics, tracing, evals
03 / Process
A streamlined process focused on clear communication, quality delivery, and long-term value.
We start with a call to understand your business goals, technical constraints, and timeline. I ask questions, listen carefully, and define a clear scope.
I design the architecture, build iteratively, and keep you in the loop with regular updates. Clean code, tested logic, documented choices.
We deploy to production with proper CI/CD, monitoring, and documentation. I ensure a smooth handover and remain available for support.
04 / Experience
A quick snapshot of where I've applied AI in production — across industrial automation, retail, consultancy, and education.
AI Engineer & Data Scientist
Sep 2025 – Present
Fortil Group (Consultant for Coesia)
Remote / Bologna, Italy
Designing GraphRAG architectures and predictive maintenance pipelines for industrial automation.
Data Scientist
Mar 2024 – Sep 2025
JAKALA
Milan, Italy
Led SAS → Python migration and delivered churn & customer value models on large-scale cloud data stacks.
Data Analytics Instructor
Jan 2024 – Present
Boolean
Milan, Italy (Remote)
Teaching Python, SQL, and ML to 30+ students per cohort with 95% satisfaction, mentoring on best practices.
Data Scientist
Sep 2023 – Mar 2024
Grandi Stazioni Retail
Milan, Italy
Owned end-to-end data pipelines and BI dashboards to monitor retail KPIs across major stations.
Data Scientist & Computer Vision Engineer
Jul 2022 – Sep 2023
Avvale
Milan, Italy
Deployed AWS-based computer vision for textile defect detection and NLP models on customer tickets.
MSc in Physics (Applied Physics)
110/110University of Milan
Specialized in applied physics with a strong focus on Machine Learning and AI techniques for data analysis and modeling.
05 / Blog
Why autoregressive inference is memory-bandwidth bound, how SLMs exploit distillation, GQA, and aggressive quantization, and how production stacks route cheap local models before touching frontier APIs.
Read articleMultiply-accumulate limits in FP Transformers, why PTQ keeps you memory-bandwidth bound, the ternary BitNet b1.58 formulation with AbsMean activations, and how BitNet.cpp maps inference to SIMD-heavy CPU kernels.
Read article06 / Case Studies
Engagements where I shipped agentic systems, GraphRAG, and ML pipelines all the way to production — with measurable outcomes.
10x faster knowledge retrieval
Reduced manual search time from hours to seconds
Architected a GraphRAG solution using LangGraph and Neo4j for an industrial automation group in the packaging sector. The system integrates knowledge graphs with LLM-powered retrieval to answer complex technical queries across thousands of process and maintenance documents.
Industrial automation · Packaging
80% automation rate
Freed 20+ hours/week of engineering time
Built autonomous AI agents using LangGraph to automate repetitive engineering workflows. Multi-agent system handles document processing, data extraction, and report generation.
Engineering workflows · Automation
95% prediction accuracy
Prevented costly equipment failures
Engineered an end-to-end MLOps pipeline for predictive maintenance using high-frequency sensor data in manufacturing plants. Containerized deployment with continuous model retraining and monitoring on cloud infrastructure.
Manufacturing · Predictive maintenance
Deployed an AWS-based deep learning system for automated textile defect detection. Built with TensorFlow/Keras, processing real-time video feeds for quality assurance and replacing manual inspection.
Textile · Quality control
Automated visual inspection process