20.10.2026 aktualisiert

**** ******** ****
Premiumkunde
60 % teilweise verfügbar

AI Engineer & CTO — LLM Agents, RAG & Production GenAI | LangGraph, PyTorch | PhD Researcher

Valencia, Spanien
Deutschland +13
info: Deutschland, Österreich, Schweiz, Großbritannien, Spanien, Frankreich, Belgien, Dänemark, Finnland, Irland, Niederlande, Norwegen, Portugal, Schweden
Valencia, Spanien
Deutschland +13
info: Deutschland, Österreich, Schweiz, Großbritannien, Spanien, Frankreich, Belgien, Dänemark, Finnland, Irland, Niederlande, Norwegen, Portugal, Schweden

Profilanlagen

cv-guillermo-llopis.pdf

Über mich

AI engineer and two-time startup CTO in Barcelona. LLM systems from prototype to production: agents, RAG, evaluation. PhD researcher in LLM hallucination detection. Available part-time only (2 to 3 days/week), remote across Europe; no full-time roles.

Skills

Fine-TuningMLOpsLangChainKünstliche IntelligenzAmazon Web ServicesKünstliche Neurale NetzwerkeCloud ComputingInformation RetrievalPythonPostgreSQLMachine LearningNatural Language ProcessingTransformerChatbotsData SciencePyTorchLarge Language ModelsMulti-Agent SystemsPrompt EngineeringDeep LearningGenerative AIKubernetesRestful ApisDocker

I build generative AI systems that reach production — not demos.


WHAT I DO

• LLM agents and multi-agent systems — LangGraph and LangChain architectures with planning, tool use and memory, split by failure mode so each stage can be evaluated and debugged independently.

• RAG over regulated document sets — mandatory citation, verification of the answer against the retrieved chunk, and an explicit "I don't have this figure" instead of a plausible invention.

• LLM evaluation and monitoring — benchmarks, hallucination detection, chain-of-thought analysis. This is also my doctoral research, so it is designed into the systems I ship rather than bolted on afterwards.

• Generative AI for biotech — diffusion models, graph neural networks and protein language models.

• Process automation with generative AI — document extraction, structured generation, end-to-end workflows.


CURRENT WORK

CTO at an AI startup, where I build the agent runtime, data pipelines and validation layers behind a document-automation platform in a regulated domain. Measured in pilots: 70% less time per reporting cycle, 3x higher response rate on automated outreach, and 100% of reported figures traceable to a source document. Where an auditor reads the output, a plausible-but-wrong answer is worse than no answer, so grounding and traceability were design constraints, not features.


In parallel, AI Research Engineer and PhD researcher at INAB Barcelona, working on LLM internal reasoning and hallucination detection.


BEFORE THAT

Co-founder and CTO of ProtForge (2023-2025), a generative AI platform for antibody design: diffusion models, GNNs and protein language models to generate and rank candidates. The engineering went into ranking rather than generation — sampling the sequence space is tractable, prioritising it well is the real bottleneck. MBA at Lanzadera in parallel.


MSc Computational Cognitive Science (University of Groningen), BSc Biomedical Engineering (UPV). Four first-place finishes in AI hackathons and startup competitions, most recently HackBarna 2025.


HOW I WORK

Remote-first from Barcelona (CET). B2B invoicing through my own Spanish company — intra-EU reverse charge, no umbrella company and no payroll overhead on your side. Available part-time immediately, full-time for long-term engagements, and able to travel within Europe when a project needs it.


STACK

Python, LangGraph, LangChain, AutoGen, CrewAI, MCP, RAG, vector databases, PyTorch, transformers, diffusion models, graph neural networks, NLP, fine-tuning (SFT, RLHF), prompt engineering, LLM evaluation, MLOps, AWS, GCP, Docker, CI/CD, PostgreSQL, REST APIs. TypeScript and Next.js when a system needs a front end.

Erfahrung

Berufserfahrung5 - 10 Jahre
Freelancer seit2026

Sprachen

EnglishverhandlungssicherFrenchGrundkenntnisseItalianGrundkenntnisseSpanishMuttersprache

Projekthistorie

Chief Technology Officer

Internet und Informationstechnologie

Building the agent runtime, data pipelines and validation layers behind a document-automation platform in a regulated domain.


• Multi-agent architecture on LangGraph: planner, data extraction, RAG over regulatory documentation and drafting — separated by failure mode so each stage can be evaluated and debugged independently.

• Grounding and traceability as design constraints: mandatory citation, verification of each answer against the retrieved chunk, and an explicit "no data" path instead of a plausible invention.

• Human validation placed only where being wrong is expensive — if everything needs review, nothing has been automated.

• Pilot results: 70% less time per reporting cycle, 3x higher response rate on automated outreach, 100% of reported figures traceable to a source document.


Python, LangGraph, RAG, LLM orchestration, data pipelines, EU data residency and audit trails.

AI Research Engineer & PhD Researcher

Pharma und Medizintechnik

Doctoral research on the internal reasoning of large language models and on hallucination detection, alongside applied work in affective computing.


• LLM interpretability and chain-of-thought analysis: what the model is actually doing when it reasons, and where that diverges from the explanation it gives.

• Hallucination detection and evaluation: benchmarks and failure analysis for generative systems.

• Neural memory models and emotion recognition for affective computing.


This research is why the production systems I build have evaluation and failure detection designed in from the start rather than retrofitted.


Python, PyTorch, transformers, NLP, LLM evaluation.

QA Automation Engineer (freelance)

Transport und Logistik

Freelance engagement on test automation for an air traffic management platform.


• Built and extended automated test coverage for a distributed, safety-relevant system.

• Worked inside the client's existing CI pipeline and review process.


Python, test automation, CI/CD.

Co-founder & Chief Technology Officer

Pharma und Medizintechnik

Co-founded and led the technical side of a generative AI platform for therapeutic antibody design.


• Generative deep learning over protein sequence and structure: diffusion models, graph neural networks and protein language models to generate and prioritise antibody candidates.

• Focused the engineering on candidate ranking rather than generation — sampling the sequence space is tractable, prioritising it well is the real bottleneck.

• Integrated computational structure validation into the candidate pipeline.

• Ran startup operations, university partnerships and industry collaborations. Completed an MBA at Lanzadera in parallel.


Python, PyTorch, GNNs, diffusion models, protein language models, AWS.

AI Teaching Assistant

Öffentlicher Dienst

Taught and supported courses across the AI and cognitive science programme.


• Deep Learning, Natural Language Processing, Machine Learning.

• Cognitive Modelling, Computational Neuroscience, Brain-Computer Interfaces.

• Ran practical sessions, marked project work and supervised student implementations.

Neurotechnology Researcher

Pharma und Medizintechnik

Machine learning research on physiological signals and medical imaging.


• ML analysis of biosignals: ECG, EMG and GSR.

• AI applied to medical imaging.


Python, signal processing, machine learning.


Zertifikate

PhD in Artificial Intelligence (in progress)

INAB Barcelona

2025

MBA

Lanzadera

2025

1st Prize - AI Hackathon HackBarna 2025

HackBarna

2025

1st Prize - VentureLab Weekend

VentureLab, University of Groningen

2023

1st Prize - DASH AI in Healthcare

DASH

2023

MSc Computational Cognitive Science

University of Groningen

2023

1st Prize - 5Groningen Student Battle

5Groningen

2022

BSc Biomedical Engineering

Universitat Politecnica de Valencia

2021


Portfolio


Kontaktanfrage

Einloggen & anfragen.

Das Kontaktformular ist nur für eingeloggte Nutzer verfügbar.

RegistrierenAnmelden