Language Engineer

European Tech Recruit; Madrid; Híbrido; EUR 50.000 - 80.000; Jornada completa

Descripción de la vacante

A top tech recruitment firm is seeking a Mid or Senior LLM Engineer to develop and optimize Large Language Models using quantum-inspired techniques. The ideal candidate will have a Ph.D. in a relevant field and 2+ to 5+ years of hands-on experience with deep learning. Strong Python, HuggingFace, and PyTorch skills are essential. This hybrid role is located in Madrid, offering a contract of 9+ months.

Formación

• 2+ years (Mid) / 5+ years (Senior) hands-on experience with transformers & deep learning.
• Solid understanding of deep learning theory, GPU architectures, and cloud environments.

Responsabilidades

• Develop and optimize LLMs using quantum-inspired techniques.
• Benchmark, evaluate, and improve model performance.
• Mentor junior engineers and share knowledge across the team.

Conocimientos

Strong Python skills

Experience with HuggingFace

Expertise in PyTorch

Familiarity with AI deployment

Understanding of deep learning theory

Familiarity with GPU architectures

Experience in cloud environments

Educación

D. in AI, CS, Data Science, or related field

Herramientas

Transformers

Descripción del empleo

LLM Engineer - Quantum + AI - hybrid from Barcelona (9 + months contract)

We’re seeking a

Mid or Senior LLM Engineer

to push the boundaries of Large Language Models and quantum-inspired AI. Develop and optimize LLMs using quantum-inspired techniques.

Benchmark, evaluate, and improve model performance.

Mentor junior engineers and share knowledge across the team.

D. in AI, CS, Data Science, or related field.

~2+ years (Mid) / 5+ years (Senior) hands‑on experience with transformers & deep learning.

~ Strong Python skills and expertise with HuggingFace, PyTorch, and AI deployment.

~ Solid understanding of deep learning theory, GPU architectures, and cloud environments.

~ Bonus Points :

• HPC experience
• RAG systems
• computer vision / audio applications
• AI ethics
• MLOps know-how

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