Senior Machine Learning Scientist - Generative AI/LLM/RAG/Python

Project overview

Senior Machine Learning Scientist - Generative AI/LLM/RAG/Python

Responsibilities

Candidates should understand not only which evaluation metrics to use, but how those metrics work, how they are calculated, their limitations and how they influence technical decisions.

The emphasis is on rapid applied research and implementation, typically within approximately two-week sprint cycles, rather than lengthy academic research programmes.

Example Project - Large-Scale Customer Review Summarisation

A representative project involves developing an intelligent Generative AI pipeline capable of processing millions of customer reviews and generating accurate, relevant and useful summaries for individual properties or destinations.

The successful candidate would be expected to:

This example illustrates the level of practical technical ownership required.

Essential Technical Skills

Machine Learning and Programming

Generative AI and NLP

Evaluation and Experimentation

Engineering and Data

Desirable Experience

Professional and Personal Attributes

We are looking for someone who demonstrates:

• Design and implement evaluation pipelines to measure model and system performance.
• Define and calculate appropriate ML evaluation metrics, including precision, recall, F1-score and other task-specific measures.
• Develop evaluation methodologies for LLM-generated outputs, including factual accuracy, relevance, completeness, consistency and hallucination detection.
• Implement automated and human-in-the-loop evaluation approaches.
• Apply LLM-as-a-judge techniques, understanding their limitations, biases and reliability.
• Evaluate retrieval quality, generation quality and overall end-to-end RAG pipeline performance.
• Develop test datasets, benchmarking approaches and repeatable evaluation processes.
• Conduct controlled experiments to compare models, prompts, retrieval strategies and pipeline configurations.
• Analyse evaluation results and use findings to improve model quality and system effectiveness.
• Balance output quality against inference latency, throughput, infrastructure requirements and operating costs.
• Applied Machine Learning Research and Problem-Solving
• Conduct highly targeted, practical research into emerging machine learning and Generative AI techniques.
• Investigate new algorithms, model architectures and evaluation methodologies.
• Translate research findings into working implementations within short development cycles.
• Develop and test proof-of-concept solutions and Minimum Viable Products (MVPs).
• Demonstrate creativity when applying ML techniques to complex business problems.
• Evaluate alternative approaches and select solutions based on measurable performance and business value.
• Work iteratively, using experimentation and feedback to improve solutions.
• Production ML Engineering and Scalability
• Develop solutions capable of processing large-scale structured and unstructured datasets.
• Optimise model inference performance, throughput, latency and resource utilisation.
• Understand the engineering trade-offs associated with model selection and deployment.
• Work with ML Engineers to integrate models into production applications.
• Apply software engineering best practices, including version control, testing, code reviews and maintainable code.
• Develop reusable components and contribute to existing ML frameworks and team codebases.
• Use data processing technologies such as SQL, Spark, Kafka and other distributed data processing systems where appropriate.
• Analyse and prepare large volumes of unstructured customer review data.
• Break the business requirement into practical ML and engineering components.
• Select suitable NLP, LLM or other machine learning approaches.
• Personally develop and implement the summarisation pipeline.
• Experiment with different models, prompts and data processing techniques.
• Build an evaluation framework to measure summary accuracy, factual consistency, relevance and completeness.
• Identify and reduce hallucinations, misleading summaries and other quality issues.
• Optimise processing efficiency, scalability, latency and inference costs.
• Collaborate with ML Engineers to support production implementation.
• Strong commercial experience developing and implementing machine learning solutions.
• Advanced hands-on Python programming.
• Practical experience training, evaluating and optimising ML models.
• Strong understanding of machine learning algorithms, statistics, experimentation and model development.
• Experience with modern ML frameworks such as PyTorch, TensorFlow, Scikit-learn or equivalent.
• Practical experience developing NLP, LLM or Generative AI applications.
• Understanding of transformer architectures, embeddings, model selection and fine-tuning approaches.
• Experience implementing end-to-end ML/GenAI pipelines.
• Knowledge of Retrieval-Augmented Generation, prompt engineering and LLM integration.
• Understanding of modern LLM development frameworks and orchestration techniques.
• Demonstrable experience personally implementing ML evaluation frameworks.
• Strong understanding of model evaluation metrics and their limitations.
• Experience designing experiments and interpreting model performance.
• Ability to evaluate LLM outputs and end-to-end pipeline effectiveness.
• Understanding of LLM-as-a-judge methodologies and quality measurement.
• Strong software engineering and clean coding practices.
• Experience working with large datasets and data processing pipelines.
• SQL and familiarity with distributed data technologies.
• Understanding of production ML systems, performance optimisation and scalability.
• Experience with Git and collaborative software development.
• Multimodal machine learning involving text, images or other data types.
• Computer Vision and advanced NLP.
• Recommendation systems and personalisation algorithms.
• Agentic AI, tool calling and intelligent assistants.
• Experience with vector databases and embedding-based retrieval.
• LLM fine-tuning, parameter-efficient fine-tuning or model adaptation.
• Experience with Java, Kafka, Hadoop or Apache Spark.
• Cloud-based ML deployment and inference optimisation.
• Research publications, patents or open-source ML contributions.
• Experience developing customer-facing AI applications at scale.
• Technical ownership: Personally builds and implements solutions rather than delegating development.
• Creative problem-solving: Knows how to apply appropriate ML techniques to achieve measurable results.
• Practical judgement: Can start with a simple MVP and progressively improve the solution.
• Technical curiosity: Understands how technologies work rather than simply knowing framework names.
• Humility: Recognises knowledge gaps, welcomes feedback and is willing to challenge assumptions.
• Learning mindset: Actively investigates new techniques and continuously develops technical expertise.
• Collaboration: Works effectively with Machine Learning Scientists, ML Engineers and Product Managers.
• Delivery focus: Balances experimentation with the need to deliver working solutions quickly.

Requirements

Strong hands-on applied Machine Learning experience will be highly valued, including candidates whose expertise is broader than Generative AI alone.

Interview Process

Stage 1 - Technical Interview

A practical ML/GenAI business case assessing the candidate's ability to:

Stage 2 - Team Fit Interview

Assessment of communication, collaboration, humility, learning mindset, adaptability and willingness to receive feedback.

Please send your CV or call Toni to discuss further.

We are an equal opportunities employment agency and welcome applications from all suitably qualified persons regardless of race, sex, disability, religion/belief, sexual orientation, or age.

We champion differences in technology recruitment and work with clients who actively wish to diversify their talent force - ALL applicants are welcome to apply

• Master's degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, Statistics, Engineering or a related quantitative discipline, or equivalent relevant experience.
• Typically an MSc with at least three years of relevant professional experience, or a PhD with at least two years of relevant experience.
• Demonstrable commercial experience applying Machine Learning to practical business problems.
• Translate business requirements into a technical solution.
• Design and explain an end-to-end ML/GenAI pipeline.
• Demonstrate detailed implementation knowledge.
• Select and justify appropriate models and techniques.
• Develop evaluation metrics and explain their limitations.
• Consider performance, scalability, latency and cost.
• Demonstrate practical problem-solving and technical decision-making.

Project details

Netherlands

6 months (1-2 days onsite weekly preferred)

Working Arrangement: Hybrid/Nearshore European candidates considered

*Urgent 10 Month Contract*

This is a *Netherlands* Hybrid based role with an excellent immediate start within a Global Technology Client that is working on *Personally designing, developing, training, testing and optimising machine learning models using Python and modern ML frameworks.*

The Opportunity

We are recruiting an experienced, highly hands-on Senior Machine Learning Scientist to join a specialist Data & AI team developing advanced Generative AI, Large Language Model (LLM) and Machine Learning solutions for customer-facing digital products.

This is a technically demanding individual contributor position requiring someone who can personally design, code, train, implement, experiment with and evaluate machine learning solutions from initial business requirements through to production.

The team develops intelligent AI applications, including conversational search, recommendation systems, automated review summarisation, intelligent assistants and multimodal machine learning solutions operating across large-scale customer datasets.

We are particularly interested in candidates who combine strong Machine Learning fundamentals with practical Generative AI development, advanced Python programming, end-to-end pipeline implementation and rigorous model evaluation.

This is not an AI management, solution architecture or purely academic research position. The successful candidate must demonstrate substantial personal involvement in developing and implementing working ML/GenAI solutions.

Key Responsibilities

Candidates must be able to explain the underlying technical architecture, the implementation of individual components and the reasoning behind their technical decisions.

A particularly important requirement is direct, hands-on experience developing and implementing evaluation frameworks for ML and Generative AI systems.

• Hands-on Machine Learning Development and Model Training
• Personally design, develop, train, test and optimise machine learning models using Python and modern ML frameworks.
• Develop production-quality, reusable and scalable machine learning code.
• Implement Natural Language Processing (NLP), Generative AI, LLM-based and potentially multimodal ML solutions.
• Apply supervised learning, deep learning, transformer-based architectures and other appropriate machine learning techniques to real business problems.
• Perform feature engineering, data preparation, model selection, hyperparameter optimisation and performance tuning.
• Experiment with different algorithms and architectures to identify effective solutions.
• Translate complex product requirements into working machine learning implementations.
• End-to-End Generative AI and RAG Pipeline Development
• Design and personally implement end-to-end ML and Generative AI pipelines.
• Develop Retrieval-Augmented Generation (RAG) solutions, including document processing, embeddings, retrieval, context construction and LLM integration.
• Implement LLM-powered applications involving summarisation, question answering, information extraction and conversational AI.
• Develop and optimise prompt engineering strategies, retrieval techniques and model orchestration.
• Build practical solutions using foundation models, fine-tuned models and other ML techniques as appropriate.
• Investigate agentic AI architectures, function calling and tool-enabled LLM workflows where relevant.
• Integrate ML components into scalable applications and collaborate with ML Engineers on production deployment.
• ML/LLM Evaluation Frameworks, Metrics and Experimentation
• Start: ASAP
• Duration: 10 Months
• Location: Amsterdam, UK, GB