Carlos Hernández Martínez

Carlos Hernández Martínez

Valencia, Spain (remote available)

Specialized in building intelligent automation and robust backend architectures. Currently focused on developing test data management systems and enterprise integration frameworks. I bridge the gap between AI innovation and software reliability by implementing quality analysis and ensuring the integrity of AI-generated code.

My Experience

QA Backend Developer

Mercadona IT

Development of an application for managing automated test data. Implementation of the integration tests framework for backend, including quality analysis with SonarQube. Ensuring the reliability of code generated by AI agents.

Python Spring Boot & Spring Batch Integration Tests Mutant tests AI Agents validation

Backend AI Engineer

Urobora SL (AI Startup)

Developed autonomous AI agents, RAG systems, and microservices in Python/FastAPI deployed on GCP with Kubernetes.

Python FastAPI LLMs & RAG GCP

Tech Stack

Backend & Core Engineering

Python Java Spring Boot Spring Batch

Artificial Intelligence

AI Agents Development Autonomous Agents Validation PyTorch Tensor Flow

QA & Software Quality

Integration FWK (Spring Boot) Mutant Testing SonarQube Playwright Postman

DevOps & Cloud

Google Cloud CI/CD (GitHub Actions, Jenkins, CloudBees) Spinnaker CloudBees

💡 Professional Competencies

Technical Leadership & Mentoring
Strategic Product Vision
Cross-functional Collaboration
Analytical Thinking & Complex Problem Solving
Adaptability in Agile & Startup Environments
International Technical Communication

Featured Projects

Medical Diagnosis System with Deep Learning - Bachelor's Thesis

Medical Diagnosis System with Deep Learning - Bachelor's Thesis

Python PyTorch TensorFlow Computer Vision Healthcare AI

Bachelor's thesis project developing an automatic classification system for knee osteoarthritis using medical radiographs. Implementation of CNN architectures (ResNet, EfficientNet) with PyTorch and TensorFlow, achieving 80% accuracy in binary classification. Application of fine-tuning techniques, data augmentation, and cross-domain validation. Focus on real clinical utility and scalability of the assisted diagnosis system.

Neural Implicit Models for Robotics - TUM Research

Neural Implicit Models for Robotics - TUM Research

Python PyTorch Robotics Research Neural Networks

Collaborative research at the Technical University of Munich (TUM) with KUKA Robotics. Development of implicit neural models for real-time collision detection in robotic manipulators. PyTorch implementation of swept volume models and optimization for efficient inference. Contribution to research paper at the intersection of Deep Learning and industrial robotics.

Academic Background

Bachelor's Degree in Computer Engineering

Polytechnic University of Valencia (UPV)

Computing specialization. Training in programming, algorithms, databases, and software architectures.

Erasmus in Computer Science

Technical University of Munich (TUM)

International exchange program focused on AI and robotics. Participation in applied research projects on implicit neural models.

Akademia Bankinter Innovation Foundation

Bankinter Innovation Foundation

Training program in innovation and technological entrepreneurship. Development of leadership skills and management of innovative projects.

Diploma in Social Innovation

CEU San Pablo Valencia

Training in social innovation and corporate responsibility in collaboration with Edwards Lifesciences.

About Me

Computer Engineer graduated from the Polytechnic University of Valencia with specialization in Artificial Intelligence and Machine Learning. Professional experience developing AI agents, enterprise testing automation, backend with Python, Spring Boot and Spring Batch, and Deep Learning models applied to real-world problems.

Technical leadership
Strategic vision
Complex problem solving
Cross-functional collaboration
Effective international communication
Continuous learning and adaptability