Senior Machine Learning Engineer (LLM)

Senior Machine Learning Engineer 6 Months+ Contract (outside IR35) Remote On behalf of a global pharmaceutical organisation, I am seeking a Senior AI/ML Engineer to help design, scale, and deploy advanced machine learning solutions that support the next generation of drug discovery. You will work closely with AI/ML scientists and life-science experts, transforming exploratory research into robust, production-grade ML pipelines. You will play a pivotal role in strengthening MLOps practices, improving scalability and reliability, and ensuring that innovative ideas deliver real-world scientific impact. If you are excited by applying AI at scale in a complex scientific environment—and want to help shape the future of AI/ML in the pharmaceutical industry—this could be your next contract! The Role: • Collaborate directly with AI/ML scientists to optimise models and deploy solutions into production, acting as an internal consultant from prototype to platform. • Design and document blueprints and best practices for transitioning research code into scalable, maintainable ML systems. • Explore, analyse, and visualise data to understand distributions and identify risks to model performance in real-world deployment. • Ensure high data quality and model reliability through data cleaning, validation strategies, and systematic testing. • Build and maintain training pipelines and reusable ML components that support scalable, repeatable ML. • Contribute to education and upskilling across teams, raising overall MLOps and ML engineering maturity. Skills/Experience required: • A collaborative, technically strong engineer with a positive mindset and a passion for applied machine learning. • PhD or Master’s degree with relevant experience, or a Bachelor’s degree with strong hands-on expertise. • Experience working closely with data scientists, data engineers, and life scientists. • Previous experience in a healthcare or life-science organisation is advantageous, but not essential. • Excellent communication skills, with the ability to explain complex technical topics to diverse audiences. • You will be highly experienced with the following: 1. Programming & ML tooling: Advanced Python skills; hands-on experience with scikit-learn, Pandas, PyTorch, Jupyter, and ML pipelines. 1. Data & platform tools: Practical knowledge of Databricks, Ray, vector databases, Kubernetes, and workflow orchestration tools such as Apache Airflow, Dagster, or Astronomer. 1. GPU & scalable infrastructure: Experience with GPU computing on-premise and/or in the cloud, including DGX systems or cloud platforms such as AWS (EKS, SageMaker) and Azure (Azure ML, AKS); familiarity with ML platforms like MLflow, ClearML, or Weights & Biases. 1. Cloud & MLOps: Strong understanding of AWS, Azure, containerisation, Kubernetes, DevOps automation, and end-to-end ML lifecycle practices. 1. Data handling: Proven ability to wrangle, process, integrate, and analyse large, heterogeneous datasets, ideally in drug discovery or biomedical contexts. 1. LLMs & generative AI: Experience with large language models, including fine-tuning, pretraining or continued pretraining, inference, RAG pipelines, and multi-agent workflows using tools such as LlamaIndex, LangChain, and vector databases. 1. Production ML: Demonstrated success building, training, and deploying production-grade machine learning models in industry and/or academic research environments. Please apply online with your CV.