Job Title: Senior Machine Learning Engineer

Location: London, 3 day's p/w onsite

Salary: £89,000 + Bonus

 

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.

 

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.

 

Essential Skills

● 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.

 

Desirable

● 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).

● Research experience or an applied ML portfolio demonstrating end-to-end model development.

● Experience mentoring colleagues and driving cross-functional process improvements.