Senior Machine Learning Engineer – LLM Systems & 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

We are seeking a talented and experienced Senior Machine Learning Engineer to join our team, focusing on Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) architectures, agents, and safety considerations.

The ideal candidate has a strong foundation in machine learning, practical engineering skills, and a passion for advancing AI systems in ambiguous, fast-paced environments.

About Our Client

Our client is a global leader in technological innovation, committed to operational excellence and impactful solutions worldwide.

Responsibilities

• Lead end-to-end machine learning projects from problem definition to deployment
• Design and implement evaluation methodologies for AI and 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 recommend practical improvements
• Build and maintain ML pipelines, tooling, and evaluation infrastructure
• Collaborate closely with product, engineering, and research teams to align ML objectives with business goals
• Prototype rapidly and iterate to solve complex business and product challenges
• Communicate technical findings, trade-offs, and recommendations to diverse stakeholders

Requirements

Required:

• 5+ years of experience in Machine Learning Engineering or related fields
• Deep understanding of machine learning fundamentals and model evaluation techniques
• Strong Python skills with experience in modern ML frameworks such as PyTorch, TensorFlow, or JAX
• Proven experience training, fine-tuning, or adapting large-scale models
• Hands-on experience working with LLMs beyond simple API integration
• Ability to evaluate AI systems and translate results into actionable insights
• Experience building and maintaining ML pipelines and systems
• Knowledge of RAG architectures, agentic systems, and AI safety concepts
• Capable of working effectively in ambiguous problem spaces with limited data and requirements
• Excellent communication skills, both written and verbal
• Willingness to work up to 9 pm Swiss time

Nice to have

• Kaggle competition winners or notable programming contest achievements
• ML modeling experience
• Experience in designing benchmarks, evaluation frameworks, or automated evaluation systems
• Experience with distributed training and large-scale inference
• Building reusable ML tooling and internal platforms
• Cloud platform expertise and modern MLOps practices
• Experience working on user-facing AI products at scale
• Research publications or experience in ML/AI research

We offer

• Opportunity to work on bleeding-edge projects
• Work with a highly motivated and dedicated team
• Competitive salary
• Flexible schedule
• Benefits package - medical insurance, sports
• Corporate social events
• Professional development opportunities
• Well-equipped office

About Us

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.