Senior ML Engineer - LLM Systems & Evaluation

Grid Dynamics; Badajoz; Presencial; EUR 55.000 - 90.000; Jornada completa

Descripción de la vacante

Grid Dynamics is seeking a Senior Machine Learning Engineer in Badajoz to lead LLM-based system design, evaluation, and RAG implementations. You will own end-to-end ML projects, develop evaluation frameworks, and collaborate across product, engineering, and research teams to translate business goals into measurable ML objectives.

The role emphasizes building scalable ML pipelines, tooling, and safe, robust AI products, with a focus on practical improvements and clear communication of results.

Formación

• 5+ years of experience in machine learning engineering or related field.
• Strong knowledge of ML fundamentals and model evaluation.
• Proficient Python programming with PyTorch, TensorFlow, or JAX.
• Experience with Large Language Models beyond API integration.
• Ability to design and implement ML pipelines and evaluation infrastructure.

Responsabilidades

• Own ML projects from problem definition through implementation.
• Design and implement evaluation methodologies for ML/AI systems.
• Create datasets, benchmarks, and metrics to measure performance.
• Evaluate and improve LLM-based systems including RAG and agents.
• Build ML pipelines, tooling, and evaluation infrastructure.
• Collaborate with product, eng, and research teams to translate goals into ML objectives.
• Prototype rapidly to solve business and product challenges.
• Communicate findings and trade-offs to technical and non-technical stakeholders.

Conocimientos

Python

ML fundamentals

LLM concepts

Model evaluation

ML pipelines

Communication

Herramientas

PyTorch

TensorFlow

JAX

Descripción del empleo

Grid Dynamics is seeking a Senior Machine Learning Engineer in Badajoz to lead LLM-based system design, evaluation, and RAG implementations. You will own end-to-end ML projects, develop evaluation frameworks, and collaborate across product, engineering, and research teams to translate business goals into measurable ML objectives.

The role emphasizes building scalable ML pipelines, tooling, and safe, robust AI products, with a focus on practical improvements and clear communication of results.