Senior ML Engineer: LLM Systems, Evaluation & Safety

Grid Dynamics; País Vasco; Presencial; EUR 70.000 - 100.000; Jornada completa

Descripción de la vacante

Grid Dynamics seeks a Senior Machine Learning Engineer to lead LLM systems and evaluation efforts, focusing on AI safety, RAG applications, and agent-based components. You will define problem statements, design benchmarks, and collaborate with cross-functional teams to translate business goals into measurable ML objectives.

Responsibilities include building pipelines, evaluating AI systems, and iterating rapidly to deliver robust ML products.

Formación

• 5+ years of experience in ML engineering or related field.
• Strong Python and ML fundamentals with model evaluation.
• Experience with LLMs and RAG, agents, and safety concepts.

Responsabilidades

• Own ML projects from problem definition through implementation.
• Design evaluation methodologies for AI/ML systems.
• Create datasets, benchmarks, and metrics to measure model and product performance.
• Evaluate and improve LLM-based systems, including RAG applications, agents, safety systems, and end-to-end AI products.
• Analyse model behaviour, identify failure modes, and recommend practical improvements.
• Build and maintain ML pipelines, tooling, and evaluation infrastructure.
• Collaborate with product, engineering, and research teams to translate business goals into measurable ML objectives.
• Prototype and iterate rapidly to solve business and product challenges.
• Communicate findings, trade-offs, and recommendations to both technical and non-technical stakeholders.

Conocimientos

Machine Learning

Python

PyTorch

TensorFlow

JAX

RAG systems

LLM safety concepts

Model evaluation

Communication skills

Herramientas

Hugging Face

ML pipelines tooling

DVC

Descripción del empleo

Grid Dynamics seeks a Senior Machine Learning Engineer to lead LLM systems and evaluation efforts, focusing on AI safety, RAG applications, and agent-based components. You will define problem statements, design benchmarks, and collaborate with cross-functional teams to translate business goals into measurable ML objectives.

Responsibilities include building pipelines, evaluating AI systems, and iterating rapidly to deliver robust ML products.