Build the right system. Before you build more of it.
A complicated stack can hide simple unanswered questions: what must the system know, which actions can it take, how will quality be measured, and what happens when it fails? Those questions should shape the architecture before infrastructure and frameworks become expensive commitments.
How we work together
I review goals, existing components, data flows, and operational constraints. Where appropriate, I explore neurosymbolic approaches that combine learned capabilities with explicit knowledge and rules. Recommendations distinguish production-ready choices from research questions, with a practical path for validating uncertain assumptions.
What the engagement can include
- Architecture review and prioritized technical recommendations
- Data, retrieval, model, and orchestration boundaries
- Evaluation strategy and failure-handling requirements
- A phased delivery roadmap with explicit trade-offs
Is this the right fit?
Suitable for a focused architecture audit, a team preparing a production build, or an ongoing fractional technical partnership. I can work independently or alongside your internal engineers, with remote collaboration and international opportunities welcome.
I’m Paolo Chignoli, a senior data scientist, AI engineer, and AI architect with an MSc in Physics from the University of Milan. I’m based in Milan and open to contractor roles and fractional partnerships with teams around the world. Remote collaboration is the default; we agree on practical arrangements together.
View my professional background on LinkedIn or explore my GitHub profile.