Senior Machine Learning Engineer

Job Title: Senior Machine Learning Engineer

Location: London, 3 day's p/w onsite

My client are 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.

• 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.

• 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.
• 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 Tensor Flow 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., Tensor Flow, PyTorch, scikit‑learn).
• Expertise with relational databases, especially Postgre
  
  SQL, 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.