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# Senior Machine Learning Engineer - LLM Systems & Evaluation

- **Company:** Grid Dynamics
- **Location:** Greater London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Distributed Computing Environment, Python (Programming Language), Machine Learning, Performance Tuning, Tensorflow, Software Safety, Cloud Platform System, Pytorch, Large Language Models, Multi-Agent Systems, Model Validation, Kaggle, Generative AI, Machine Learning Operations
- **Published:** September 9, 2026
- **Apply:** https://www.collegerecruiter.com/job/2854918186-senior-machine-learning-engineer--llm-systems--evaluation

## About the Role

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., * 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, * 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

## Description

* 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


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