Carlos Maestre
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AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI Director / AI Engineering Manager
Since the beginning of my professional journey I wanted to build an end-to-end profile, having a pivot role between product, development and business communications, ensuring a tangible business impact. With a robust academic background, including both a Master's and a PhD in AI and ML, my focus has centered on building models and orchestrating their deployment into production environments, always with a keen eye on user-centricity and real-world applicability. Transitioning into leadership roles, I've had the opportunity to lead teams in the development of successful AI solutions. I've applied software engineering methodologies to AI projects, mastering Scrum together to other software development techniques as eXtreme Programming. My experience as people management has equipped me with the skills to foster collaboration, drive motivation, and create the best possible environment for the team members. Furthermore, my proficiency in stakeholder management has been instrumental in navigating complex organizational landscapes, enabling effective communication and collaboration between technical teams, product managers, and business stakeholders. By working closely with cross-functional teams, I've been able to ensure that our AI initiatives are not only technically sound but also strategically aligned with the broader goals of the organization.
Sorbonne Université
Doctor of Philosophy - PhD, Artificial Intelligence and Robotics
January 1, 2014 – January 1, 2018
Universidad Politécnica de Madrid
Master of Research in Artificial Intelligence (AI) - BAC + 6, Robotics
January 1, 2013 – January 1, 2014
Grupo Femxa
Project management
January 1, 2011 – January 1, 2011
Ibertech SL
Java SE and EE
January 1, 2009 – January 1, 2009
Universidad Politécnica de Madrid
Bsc. and MSc. Degree in Computer Science, Major in Software Engineering - BAC + 5
N/A – Present
Fever
AI lead
April 1, 2026 – Present
Nfq Advisory, Solutions, Outsourcing
AI Team Manager / Senior Tech Lead
October 1, 2024 – March 1, 2026
Madrid, Community of Madrid, Spain
Fiction Express Education
CDO and AI/SWE team manager (Lead data scientist)
June 1, 2023 – October 1, 2024
Madrid, Community of Madrid, Spain
DATAIS
AI advisor
March 1, 2022 – October 1, 2024
Madrid, Community of Madrid, Spain
Testlio
Head of AI | AI and data team manager
September 1, 2021 – June 1, 2023
Madrid, Community of Madrid, Spain
Frontiers
Senior Machine Learning Engineer
July 1, 2020 – July 1, 2021
Madrid, Community of Madrid, Spain
Valeo
Machine Learning Engineer
September 1, 2018 – June 1, 2020
París
UPMC - Sorbonne Universities
Teaching Assistant
January 1, 2015 – October 1, 2017
París y alrededores, Francia
ISIR
Ph.D. Student | Robotics and Artificial Intelligence
November 1, 2014 – April 1, 2018
París y alrededores, Francia
Atos
Technical coordinator / Researcher
January 1, 2012 – September 1, 2013
Madrid
Amaris
Research And Development Engineer
November 1, 2010 – January 1, 2012
Thales Communications
Junior Researcher
March 1, 2010 – October 1, 2010
ISBAN
Internship
October 1, 2008 – July 1, 2009
Fundación Esquerdo
Computer instructor
January 1, 2003 – September 1, 2008
DREAM
January 1, 2015 – March 1, 2019
DREAM is a robotic project that incorporates sleep and dream-like processes within cognitive architecture. This enables an individual robot or groups of robots to consolidate their experience into more useful and generic formats, thus improving their future ability to learn and adapt. DREAM relies on Evolutionary Neurodynamic ensemble methods as a unifying principle for discovery, optimization, re-structuring and consolidation of knowledge. This new paradigm will make the robot more autonomous in its acquisition, organization and use of knowledge and skills just as long as they comply with the satisfaction of pre-established basic motivations.
SmartAgriFood
March 1, 2011 – September 1, 2013
The SmartAgriFood project addresses the food and agribusiness as a use case for the Future Internet. The intelligence, efficiency, sustainability and performance of the agri-food sector can be radically enhanced by using information & decision support systems that are tightly integrated with advanced internet-based networks & services. Concurrently, the sector provides extremely demanding use cases for Future Internet design from physical layer all the way up to the service layer. This project will focus on three sub systems of the sector – smart farming, focussing on sensors and traceability; smart agri-logistics, focusing on real-time virtualization, connectivity and logistics intelligence; and smart food awareness, focussing on transparency of data and knowledge representation. Using a user-centred methodology, the use case specification will be developed with a particular focus on transparency and interoperability of data and knowledge across the food supply chain.
Smart Agri-food
September 1, 2010 – September 1, 2012
The SMARTAGRIFOOD project - Aims to boost the application and use of future internet ICTs in the Agri-Food domain - Will increase the competiveness of the European Agri-Food domain - Will effect a huge number of SMEs in the Agri-Food domain throughout Europe
Claude Code in Action
Anthropic
June 24, 2026 – Present
Generative AI with Large Language Models
Coursera
June 24, 2026 – Present
Structuring Machine Learning Projects
Coursera
June 24, 2026 – Present
Neural Networks and Deep Learning
Coursera
June 24, 2026 – Present
Convolutional Neural Networks
Coursera
June 24, 2026 – Present
Deep Learning Specialization
Coursera
June 24, 2026 – Present
Software Processes and Agile Practices
University of Alberta
June 24, 2026 – Present
Scrum 101- A fundamental look at the agile Scrum Master role
Udemy
June 24, 2026 – Present
Agile Planning for Software Products
University of Alberta
June 24, 2026 – Present
Sequence Models
Coursera
June 24, 2026 – Present
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project experience, particularly in robotics and agri-food, shows a breadth of application domains for AI. Their leadership roles in various companies (Fever, Nfq Advisory, Fiction Express Education, Testlio) suggest adaptability and a proactive approach to building and scaling AI capabilities. The academic background and research experience align well with a culture that values innovation and continuous learning. However, the projects listed are primarily personal/research-oriented with no specific technologies mentioned, which might indicate a gap in demonstrating practical application diversity in a corporate setting beyond their work experience.
Soft Skills & Operational Fit
The candidate demonstrates strong leadership, team management, and project coordination skills, evidenced by their roles as AI Lead, AI Team Manager, and Head of AI. They have experience in setting up team processes (Scrum, Kanban), stakeholder management, and defining company-wide AI/ML strategies. Their background also indicates an ability to work in research-intensive environments and manage complex technical projects.