Senior AI Engineer · Data Scientist
Available for B2B Consulting, Contract Roles, and Freelance Projects.
I partner with US and European teams to architect Knowledge Graphs, AI agents, and production RAG systems that actually ship — currently as an independent contractor for PwC, building enterprise-scale graph solutions for the US market.
Trusted by industry leaders
0+
Years in production AI
0+
Projects shipped to production
0x
Faster knowledge retrieval
0
Cloud & graph certifications
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 GDS Certified
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.
Senior Data Scientist & AI Engineer
May 2026 – Present
PwC (Independent Contractor)
United States · Remote
B2B consulting for enterprise-level AI projects targeting the US market. Architecting and deploying highly scalable Knowledge Graphs and AI solutions. R&D on agentic AI frameworks and graph database integrations.
AI Engineer & Data Scientist
Sep 2025 – Apr 2026
Fortil Group (Consultant for Coesia)
Remote / Bologna, Italy
Designed 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
68% of companies blew their AI agent budget this year. 40% of agentic projects will be cancelled by 2027. The difference between agents that ship and agents that sink is not the model — it is the 14-point production checklist that most teams skip. Here it is, with the architecture I deploy for every engagement.
Read articleMost companies use graph databases as glorified foreign key stores. They store relationships but never analyze them. Here are five production-ready graph algorithms, each mapped to a concrete business outcome, with Python and Neo4j code you can deploy this week.
Read article06 / Case Studies
Engagements where I shipped agentic systems, GraphRAG, and ML pipelines all the way to production — with measurable outcomes.
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
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
07 / Engagement
Clear, outcome-driven engagement models — pick the one that fits where you are, and we scope the rest on a discovery call.
Best for: Teams deciding what to build
A focused review of your data, models, and infrastructure to find the highest-ROI opportunities and de-risk the build before you commit budget.
Best for: Shipping a real system
End-to-end design and delivery of GraphRAG, agentic systems, or ML pipelines — built to production standards, not demos.
Best for: Ongoing momentum
Embedded senior AI engineering for teams that need consistent delivery — I own outcomes, raise the bar, and mentor your engineers.
08 / FAQ
The things teams usually want to know before reaching out. Anything else? Just ask my AI or send a message.
Production AI and data work: GraphRAG and retrieval systems, autonomous AI agents (LangGraph), MLOps pipelines, LLM integration and fine-tuning, computer vision, and data engineering. I focus on systems that ship to production with monitoring and evaluation — not throwaway prototypes.