Carlos Hernández Martínez

{"basic_info":{"name":"Carlos Hernández Martínez","image_path":"me.jpg","personal_image_alt":"Carlos Hernández Martínez","role":"AI QA Engineer & Backend Developer","emails":["chermar.pro@gmail.com","carlos.cbm32@gmail.com"],"linkedin":"https://linkedin.com/in/carl0shdez","github":"https://github.com/Tempus23","location":"Valencia, Spain","tagline":"Bridging the gap between AI innovation and software reliability."},"about_me":{"description_paragraphs":["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."],"attributes":["Research-to-production mindset","End-to-end ownership","Autonomous problem-solving","Cross-disciplinary collaboration","International team communication","Fast ramp-up & self-directed learning"]},"professional_experience":[{"date":"Jul. 2025 - Present","title":"QA Backend Developer","company":"Mercadona IT","description":"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.","keywords":["Python","Spring Boot & Spring Batch","Integration Tests","Mutant tests","AI Agents validation"]},{"date":"Oct. 2024 - Jun. 2025","title":"Backend AI Engineer","company":"Urobora SL (AI Startup)","description":"Developed core AI infrastructure for an early-stage startup: autonomous agents for task automation, RAG systems for enterprise knowledge management, and microservices architecture on GCP/Kubernetes. Worked end-to-end from research to production deployment.","keywords":["Python","FastAPI","LLMs & RAG","GCP"]}],"academic_formation":[{"date":"2021-2025","title":"Bachelor's Degree in Computer Engineering","company":"Polytechnic University of Valencia (UPV)","description":"Specialization in Artificial Intelligence and Machine Learning. Training in algorithms, data structures, software architectures, and machine learning systems."},{"date":"6 months, 2024","title":"Erasmus in Computer Science","company":"Technical University of Munich (TUM)","description":"International exchange program focused on AI and robotics. Participation in applied research projects on implicit neural models."},{"date":"2023","title":"Akademia Bankinter","company":"Bankinter Innovation Foundation","description":"Training program in innovation and technological entrepreneurship. Development of leadership skills and management of innovative projects."},{"date":"2019-2020","title":"Diploma in Social Innovation","company":"CEU San Pablo Valencia","description":"Training in social innovation and corporate responsibility in collaboration with Edwards Lifesciences."}],"skills":{"technical":[{"category":"Backend & Core Engineering","items":["Python","Java","Spring Boot","Spring Batch"]},{"category":"Artificial Intelligence","items":["AI Agents Development","Autonomous Agents Validation","PyTorch","TensorFlow"]},{"category":"QA & Software Quality","items":["Integration FWK (Spring Boot)","Mutant Testing","SonarQube","Playwright","Postman"]},{"category":"DevOps & Cloud","items":["Google Cloud","CI/CD (GitHub Actions, Jenkins, CloudBees)","Spinnaker"]}],"competencies":["Technical Leadership & Mentoring","Strategic Product Vision","Cross-functional Collaboration","Analytical Thinking & Complex Problem Solving","Adaptability in Agile & Startup Environments","International Technical Communication"]},"languages":[{"name":"Spanish","level":"Native"},{"name":"English","level":"B2/C1 (Cambridge Certificate)"}],"projects":[{"title":"Medical Diagnosis System with Deep Learning - Bachelor's Thesis","description":"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.","link":"","github":"https://github.com/Tempus23/Radiography_TFG","image":"projects/rodilla-artrosis.png","inProgress":false,"tags":["Python","PyTorch","TensorFlow","Computer Vision","Healthcare AI"]},{"title":"Neural Implicit Models for Robotics - TUM Research","description":"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.","link":"","github":"https://github.com/Amrsaeed/FastCollisionDetection","image":"projects/sweept-volume.png","inProgress":false,"tags":["Python","PyTorch","Robotics","Research","Neural Networks"]}],"footer_info":{"copyright_name":"Carlos Hernández Martínez","contact_email":"chermar.pro@gmail.com","about_me_link":"/mi-portfolio/#about-me"}}