Enrique Manjavacas
Senior NLP Engineer at Sigma Cognition
Madrid, Community of Madrid, Spain
Summary
👤
Senior🎓
Top SchoolEnrique Manjavacas is a Senior NLP Engineer based in Madrid with 11 years of experience building and deploying language technologies, from academic research to production LLMOps. He has a PhD in Computational Linguistics and a track record of creating domain-adapted LLMs (e.g., MacBERTh, GysBERT) and applying them to tasks like WSD, periodization, NER and few-shot learning in low-resource settings. In industry he’s shipped end-to-end ML pipelines and agentic workflows that coordinate LLMs, tools and APIs, and implemented parameter-efficient fine-tuning for varied domains including historical, literary and legal text. His open-source contributions include optimizing attention implementations in the DyNet toolkit, showing a hands-on fluency with model internals and efficiency-focused refactors. Comfortable moving models from experimentation to reliable production, he blends deep linguistic expertise with practical engineering for real-world NLP systems. An unusual strength is his ability to bridge literary and computational perspectives, having worked extensively on creative text generation and historical language resources.
11 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD, Computational Linguistics, Doctor of Philosophy - PhD, Computational Linguistics at University of AntwerpMaster's degree, Linguistics, Master's degree, Linguistics at Freie Universität BerlinBachelor of Arts - BA, Classics and Classical Languages, Literatures, and Linguistics, Bachelor of Arts - BA, Classics and Classical Languages, Literatures, and Linguistics at Universidad Complutense de Madrid
English, German, Spanish, Dutch, Polish, Romanian
Github Skills (9)
attention-mechanism10
neural-network10
tensorrt10
machine-learning10
tensor10
tensorflow10
dynet10
python10
operation10
Programming languages (11)
Github contributions (5)
clab/dynet
Jan 2017 - Jan 2017
DyNet: The Dynamic Neural Network Toolkit
Role in this project:
ML Engineer
ML EngineerContributions:5 commits, 4 PRs, 13 comments in 5 days
Contributions summary:Enrique primarily focused on optimizing and refactoring code within the `examples/python/attention.py` file, suggesting a focus on machine learning model implementation. They refactored the attention mechanism by vectorizing loops and externalizing computations. Further contributions involved replacing random sampling with argmax, and fixing reshape documentation, indicating experience with the underlying library functions. These changes likely improved the efficiency or accuracy of the attention model within the DyNet toolkit.
dynetdynamic-neural-networkdeep-learningneural-networksmachine-learning
emanjavacas/retrieve
Jun 2020 - Jun 2022
Contributions:77 pushes, 1 branch, 7 tags in 2 years 1 month
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