Senior ML Engineer: LLM Systems & RAG Evaluation

Grid Dynamics; Greater London; On-site; GBP 90,000 - 140,000; Full time

Benefits offered by this job

Competitive salary

Flexible schedule

Medical insurance

Sports

Corporate events

Professional development opportunities

Well-equipped office

Job summary

Grid Dynamics seeks a Senior Machine Learning Engineer focused on LLMs, RAG architectures, agents, and safety. The role demands a strong ML foundation and practical engineering skills to advance AI systems in fast-paced environments.

The candidate will lead projects end-to-end, design evaluation methodologies, and collaborate with cross-functional teams to translate ML insights into business outcomes.

Qualifications

• 5+ years of experience in Machine Learning Engineering or related fields.
• Strong foundation in machine learning fundamentals and evaluation techniques.
• Proficient Python with ML frameworks (PyTorch, TensorFlow, or JAX).
• Experience training, fine-tuning, or adapting large-scale models.
• Hands-on experience with LLMs beyond API integration.

Responsibilities

• Lead end-to-end ML projects from problem definition to deployment.
• Design evaluation methodologies for AI/ML systems.
• Develop datasets, benchmarks, and metrics to measure performance.
• Evaluate and optimize LLM-based systems including RAG, agents, and safety modules.
• Analyze model behaviors, identify failure modes, and propose improvements.
• Build and maintain ML pipelines, tooling, and evaluation infrastructure.
• Collaborate with product, engineering, and research teams to align ML goals with business needs.
• Prototype rapidly and iterate to solve complex business challenges.
• Communicate findings and trade-offs to diverse stakeholders.

Skills

Python

PyTorch

TensorFlow

JAX

LLMs

RAG

AI Safety

ML Pipelines

Communication

Swiss Time Work Hours

Tools

PyTorch

TensorFlow

JAX

Job description

Grid Dynamics seeks a Senior Machine Learning Engineer focused on LLMs, RAG architectures, agents, and safety. The role demands a strong ML foundation and practical engineering skills to advance AI systems in fast-paced environments.

The candidate will lead projects end-to-end, design evaluation methodologies, and collaborate with cross-functional teams to translate ML insights into business outcomes.