Experience

Different contexts, a recurring pattern.

I have worked in research, industry, public administration, energy, financial services, and technology product development. In those contexts I have addressed problems where it was necessary to first understand the situation, investigate alternatives, and build an applicable response.

My experience combines applied research, software engineering, data, artificial intelligence, systems design, product, capability building, and governance.

Scientific research and complex systems

I work with problems where it is first necessary to understand the phenomenon, the data, and the relationships before choosing a technique.

Scientific data exploration

Analysis of large scientific datasets from observations and simulations, using clustering, dimensionality reduction, and other unsupervised techniques to identify non-obvious patterns and structures.

Robotics and physical systems

Modeling, simulation, perception, and navigation in mobile robotic systems, combining software, environment representation, and behavior in physical systems.

Knowledge representation

Construction of ontologies, reasoning, and semantic matching to represent knowledge and connect information from different sources.

Industry and operations

In industry, a solution cannot be evaluated only by its technical accuracy: it must integrate into real processes and work under operational constraints.

Industrial visual quality

Design and development of a vision system to automate a manual quality-control task, including image acquisition, annotation, analysis, and validation of the approach.

Anomaly detection

Application of statistical and machine-learning models to detect anomalous behavior in industrial systems in R&D contexts.

OT systems

Conceptualization of a platform to integrate operational signals and cybersecurity evidence through an event-driven architecture, state reconstruction, and progressive generation of operational knowledge.

Data, knowledge, and artificial intelligence

Artificial intelligence has been a constant throughout much of my career, but applied from the problem rather than from the technology.

NLP and semantic search

Design of systems to search and relate information through natural language processing, embeddings, and vector databases.

Generative AI and agents

Design and development of LLM- and agent-based solutions to analyze complex documentation, generate specialized information, interact with document content, and support decision processes.

Advanced document management

Design of platforms combining document processing, information extraction, RAG, GraphRAG, and knowledge-based reasoning to validate requirements and resolve ambiguities.

Prediction and optimization

Application of machine learning and time series to prediction, planning, and optimization problems over operational data.

Computer vision with limited labeling

Design of approaches for situations where visual evidence is small, distributed, or weakly labeled, using deep learning, self-supervised learning, transformers, or multi-instance learning when the problem requires it.

Product and capability design

A recurring part of my work is moving from a specific need to a solution with enough structure to evolve.

Reusable tools

Design and production deployment of generalizable tools from concrete needs, identifying which part of the solution can be reused in other problems.

Platforms and MVPs

Conceptualization of platforms, architecture definition, MVP construction, and design of the later transition toward real and production environments.

Event-driven systems

Exploration and development of models based on events, history, and state to reconstruct the behavior of complex systems and generate traceable operational knowledge.

Organization, governance, and launch

Technology alone does not create a capability. Processes, responsibilities, decision criteria, QA, and operating mechanisms are also needed.

Project lifecycle

Definition of working guides from discovery and functional analysis to deployment, operation, and support.

QA

Definition of QA criteria and processes covering application, architecture, data, interfaces, security, resilience, observability, CI/CD, and operation.

Governance

Design of models to structure how a technology unit operates, how initiatives enter and are evaluated, and how daily work is managed.

Education

Technical and business education

Degree in Computer Science.
Research training in Artificial Intelligence, systems, and robotics.
Master's degree in Computational Engineering and Intelligent Systems.
MBA in Business Administration and Management.

This combination allows me to work across levels that are usually separated: domain, engineering, artificial intelligence, product, organization, and business.