Lead developer / ML & AI engineer
Public-Sector AI R&D · IFCE Innovation Hub
Applied ML & LLM research on public-sector financial data
An applied AI/ML research-and-development engagement at IFCE's Innovation Hub, supported by EMBRAPII, building machine-learning and LLM-based tooling for public-sector financial data. Project specifics are covered by a confidentiality agreement and are not disclosed here.
A research-and-development engagement at the IFCE Innovation Hub, with EMBRAPII support, applying machine learning and large-language-model techniques to the analysis of public-sector financial data. My contribution spanned data engineering, ML modeling and LLM-based analytical tooling on a Python/Django stack. The project's methods, components and results are protected by a confidentiality agreement, so this entry describes only the nature of the work and its institutional context — not its internals.
Problem
Public-sector financial data is fragmented and hard to analyze at scale, and any automated assessment has to stay explainable and auditable.
Approach
Applied machine learning and LLM techniques on a Python/Django stack, within an EMBRAPII-supported innovation project. Specific methods and components are under a confidentiality agreement.
Outcome
Delivered as part of an EMBRAPII-supported R&D project at IFCE's Innovation Hub. Further detail is withheld under confidentiality.
Highlights
- Applied machine learning and LLM techniques to public-sector financial data
- Data engineering and analytical tooling on a Python / Django stack
- Conducted within IFCE's EMBRAPII-supported Innovation Hub
- Methods, components and results withheld under a confidentiality agreement
Metrics
- Institutional context
- IFCE Innovation Hub · EMBRAPII
- Confidentiality
- Project under NDA
Stack
- Python
- Django
- Machine learning
- LLM-based tooling
- RESTful APIs
- uv