Senior / Mid LLM Engineer – Applied AI & Model Optimization
San Sebastián (Spain) | Hybrid

About the opportunity
We are partnering with a fast-scaling deep-tech company operating at the intersection of advanced AI, large language models, and next-generation computational techniques.

The team focuses on solving real-world, high-impact problems by building and optimizing AI systems used by global enterprises across industries such as finance, energy, and industrial operations.

This is not a pure research role — it’s applied AI at scale, where your work directly translates into production-ready solutions.

Role mission
As an LLM Engineer, you will play a key role in designing, improving, and deploying large language models, with a strong focus on efficiency, scalability, and real-world applicability.

You’ll work on challenging problems such as model compression, performance optimization, and integration into complex systems, collaborating with highly technical teams in a fast-paced environment.

What you’ll be doing
Design and improve LLM architectures and optimization techniques for real-world use cases.
Fine-tune and evaluate transformer-based models to enhance performance and efficiency.
Work on model compression and acceleration strategies.
Run benchmarks, analyze model behavior, and identify performance bottlenecks.
Contribute to the deployment of AI models in production environments.
Collaborate with cross-functional teams (engineering, data, product).
Mentor junior engineers and contribute to technical decision-making.
Stay up to date with the latest advancements in LLMs and applied AI.

What we’re looking for
Background in Computer Science, AI, Data Science or related field (Master’s or PhD preferred).
Hands-on experience working with:
Large Language Models / Transformers
PyTorch and modern ML frameworks
Hugging Face ecosystem or similar
Strong understanding of deep learning fundamentals (training & inference).
Solid programming skills in Python.
Experience working with GPUs and performance optimization.
Familiarity with deploying models in cloud environments.
Strong problem-solving mindset and ability to work in complex systems.
Fluent English.

Nice to have
Experience with:
Model compression / inference optimization
RAG systems
Large-scale training or HPC environments
MLOps / production pipelines
Docker / cloud infrastructure
Research background or publications in AI
Experience working on applied AI projects in industry