Senior Machine Learning Engineer

The Role: On behalf of a global pharmaceutical organisation, I am seeking a Senior Machine Learning Engineer to help scale and operationalise AI/ML innovation. You will work at the interface of cutting-edge data science and robust engineering, partnering closely with AI/ML scientists to transition exploratory research into production-ready, repeatable ML solutions. This is an amazing opportunity to immerse yourself in a vibrant tech ecosystem while contributing to the transformation of AI/ML in the pharmaceutical industry. Role Responsibilities: Partner directly with AI/ML scientists to optimise models and deploy solutions into production, acting as an internal consultant from prototype to platform. Translate exploratory work into robust ML pipelines, creating blueprints and best practices for scalable, repeatable machine learning. Explore, analyse, and visualise data to understand distributions and identify issues that may impact real-world model performance. Ensure data quality and model reliability through validation strategies, cleaning pipelines, and systematic testing. Build and improve training pipelines and reusable ML components, addressing errors and technical debt. Collaborate with ML Infrastructure engineers to co-develop ML platforms, strengthen MLOps capabilities, and upskill teams across the organisation.   Skills/Experience required: You are a technically strong, collaborative engineer with experience working alongside data scientists and life-science researchers. PhD or Master's degree with relevant experience, or a Bachelor's degree with strong, hands-on expertise in ML engineering. Experience working in a healthcare or life-science environment would be advantageous, but not essential. Advanced Python skills and hands-on experience with data analytics and deep learning tools such as scikit-learn, Pandas, PyTorch, Jupyter, and ML pipelines. Practical experience with modern data and ML tooling, including Databricks, Ray, vector databases, Kubernetes, and workflow orchestrators such as Apache Airflow, Dagster, or Astronomer. Experience with GPU computing, on-premise and/or in the cloud, and building end-to-end scalable ML infrastructure. Strong knowledge of AWS and/or Azure, containerisation, Kubernetes, automation/DevOps, and the full ML lifecycle. Practical expertise in data wrangling and integration of large, heterogeneous datasets relevant to drug discovery. Hands-on experience with large language models, including fine-tuning, DPO, training, hosting, RAG pipelines, vector databases, and multi-agent systems. A proven track record of building, training, and deploying production-grade ML models in industry and/or academic research.