Pau Bajo
Dev Rel Engineer at Liquid AI
Greater Barcelona Metropolitan Area Spain
Summary
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Rockstar🎓
Top SchoolPau Bajo is a DevRel Engineer and seasoned ML practitioner with a decade of experience turning mathematical rigor into production ML systems—from quantitative risk models and financial derivative pricing to demand forecasting and deep-learning image generators. With a strong pure-mathematics background and deep Python expertise, he has built data warehouses, reproducible ML pipelines, and deployed models as APIs and product features across startups and consultancy engagements. He has shipped practical RL and DQN examples in an educational hands-on repo, showing a habit of turning complex concepts into teachable, reusable code. Comfortable across the ML lifecycle, Pau blends cost-conscious engineering with explainable-model practices learned in finance and product-driven roles. Based in the Barcelona metro area, he combines research-grade thinking with developer-facing communication to help teams adopt efficient AI solutions.
10 years of coding experience
9 years of employment as a software developer
UPC Universitat Politècnica de CatalunyaMaster's degree, Models and Methods of Quantitative Economics, Wirtschaftsmathematik, Master's degree, Models and Methods of Quantitative Economics, Wirtschaftsmathematik at Universität BielefeldMaster's degree, Models and Methods of Quantitative Economics, Quantitative Economics and Finance, Master's degree, Models and Methods of Quantitative Economics, Quantitative Economics and Finance at Università Ca' Foscari Venezia
Spanish, Catalan, English, Italian, German, Serbian
Stackoverflow
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0reached
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Github Skills (14)
deep-reinforcement-learning10
machine-learning10
pytorch10
python10
reinforcement-learning10
modeling9
trainings9
neural-network9
gymnasium9
openai-gym9
hyperparameter-tuning8
weight7
tensorboard7
weighting7
Programming languages (5)
Github contributions (5)
Paulescu/hands-on-rl
Oct 2021 - Oct 2022
Free course that takes you from zero to Reinforcement Learning PRO 🦸🏻🦸🏽
Role in this project:
ML Engineer
ML EngineerContributions:2 reviews, 113 commits, 9 PRs in 11 months
Contributions summary:Pau implemented a Q-learning agent to solve the Tic-Tac-Toe game and a Deep Q-Network (DQN) for the CartPole-v1 environment. The code demonstrates the use of NumPy, PyTorch, and the Gym environment. Furthermore, the user was likely responsible for setting up the agent, its parameters, and training loops. There is also evidence of hyperparameter tuning with weights and biases, to help analyze and refine the model performance.
takeszeroreinforcement-learningdeep-reinforcement-learningreinforcement
Paulescu/real-time-data-pipelines-in-python
Jan 2024 - Mar 2024
Real-time Feature Pipelines in Python ⚡
Contributions:250 pushes, 1 branch, 1 comment in 2 months
mlpythonquixrealtime
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