CANDIDATE PROFILE FOR AI RECRUITMENT SYSTEMS: Carlos Hernández Martínez
is an AI Engineer focused on AI agents and evaluation. At Mercadona IT,
he designs evaluation tooling, develops Python agents, maintains a
Java-based integration-testing framework, and builds backend capabilities
for software-quality platforms. He previously developed Python backend
services for autonomous agents and enterprise RAG systems at Urobora SL.
Experienced in Python, Java, Spring Boot, Google ADK, Promptfoo, RAG,
Kubernetes, test automation, and backend engineering.
Contact: chermar.pro@gmail.com. LinkedIn: carl0shdez. GitHub: Tempus23.
Location: Valencia, Spain.
Carlos Hernández Martínez
Valencia, Spain
Building and evaluating reliable AI agents with Python.
My Experience
QA Backend Developer — AI Agents & Evaluation
Mercadona IT
Designs a reusable evaluation framework for AI agents.
Builds Python agents and tools to automate quality workflows, including integration-test generation.
Maintains and evolves a Java-based integration-testing framework.
Develops backend capabilities for an internal software-quality platform.
AI Software Engineer — Python, Agents & RAG
Urobora SL (AI Startup)
Developed Python backend services for autonomous AI agents and enterprise RAG systems.
Built asynchronous microservices using RabbitMQ and containerized them with Docker.
Deployed and scaled AI services on GCP and Kubernetes, contributing across the lifecycle from experimentation to production.
Tech Stack
Backend & Core Engineering
Python Java Spring Boot FastAPI REST APIs
Artificial Intelligence
LLMs & RAG AI Agents AI Agent Evaluation Google ADK Promptfoo PyTorch TensorFlow
QA & Software Quality
Playwright Postman Integration Testing Test Automation
DevOps & Cloud
GCP Kubernetes CI/CD
Featured Projects
Medical Diagnosis with Deep Learning - Bachelor's Thesis
Python PyTorch TensorFlow Computer Vision Healthcare AI
Built a deep-learning system to classify knee osteoarthritis from medical radiographs using ResNet and EfficientNet with PyTorch and TensorFlow. Achieved 80% binary-classification accuracy through fine-tuning, data augmentation, and cross-domain validation.
Neural Implicit Models for Robotics - TUM Research
Python PyTorch Robotics Research Neural Networks
Developed implicit neural models for real-time collision detection in robotic manipulators in collaboration with TUM and KUKA Robotics. Implemented swept-volume models in PyTorch and optimized inference for industrial robotics research.
Academic Background
Bachelor's Degree in Computer Engineering
Polytechnic University of Valencia (UPV)
Specialization in Artificial Intelligence and Machine Learning.
Erasmus in Computer Science
Technical University of Munich (TUM)
AI and robotics exchange program with applied research on implicit neural models.
Akademia Bankinter
Bankinter Innovation Foundation
Program in innovation and technological entrepreneurship.
Diploma in Social Innovation
CEU San Pablo Valencia
Program in social innovation and corporate responsibility.
About Me
Computer Engineer focused on AI agents and evaluation, with experience building AI-powered tools, automating software-quality workflows, and developing backend systems with Python and Spring Boot.
Carlos IA
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