Senior Machine Learning Engineer

Location: London, 3 days p/w onsite

My client is a global, independent digital-focused research and analytics organisation operating across EMEA, North America, and APAC. Their work combines media strategy, data science, qualitative research, and engineering to help clients make confident, data-driven decisions.

The Team

You will be an integral member of the Product & Engineering and Data Science teams. The structure empowers individuals and creates meaningful scope to contribute and influence outcomes. Teams collaborate closely across Data Science, Research, Engineering, and Finance in multiple regions. The culture places strong emphasis on honesty, fairness, curiosity, and continuous learning. Multidisciplinary expertise and knowledge sharing are core to how the teams operate.

The Role

• Lead MLOps initiatives, defining and implementing scalable processes to automate model training, deployment, and monitoring.
• Co-develop machine learning models with Data Scientists from experimentation through to production, contributing to architecture, training strategy, tuning, and evaluation.
• Design, build, and evaluate ML models (e.g., classification, regression, NLP, clustering) to address business challenges, owning the full development lifecycle.
• Lead experimentation cycles, including A/B testing, benchmarking, and performance evaluation against business KPIs.
• Build and maintain pipelines and frameworks for data versioning, feature engineering, and automated retraining within a cloud environment.
• Collaborate with Engineering and Data Science teams to organise and optimise model-related data while balancing performance and accuracy needs.
• Lead ML engineering tasks including feature engineering, model optimisation, model selection, and integration into production systems.

Requirements

• 6+ years’ experience as a Software Engineer, ML Engineer, or MLOps Engineer.
• Expertise with cloud technologies (e.g., GCP or equivalent).
• Strong understanding of the ML lifecycle, including deployment frameworks such as TensorFlow Serving or similar.
• Hands-on experience building, training, and evaluating ML models (classification, regression, NLP, time series, etc.)—not limited to deployment.
• Solid understanding of statistical modelling, experimental design, and model evaluation metrics (precision, recall, AUC, RMSE, etc.).
• Proficiency in Python with strong experience using ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
• Expertise with relational databases, especially PostgreSQL, including advanced schema design and query optimisation.
• Familiarity with CI/CD, containerisation (Docker), and orchestration tools (Kubernetes).
• Strong numerical and analytical skills.
• Excellent written and verbal communication, with a proactive and collaborative approach.
• Practical experience working with large language models (LLMs) in data or ML pipelines.
• Experience with DuckDB or columnar file systems such as Apache Parquet.
• Experience with DBT or similar data transformation frameworks.
• Experience with model monitoring tools (e.g., MLflow, Evidently) and model explainability frameworks.
• Experience with ML experimentation and tracking platforms (e.g., Weights & Biases, Neptune, MLflow Tracking).
• Experience mentoring colleagues and driving cross-functional process improvements.