Machine Learning Engineer

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., * 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.

• 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.
2. Data & platform tools: Practical knowledge of Databricks, Ray, vector databases, Kubernetes, and workflow orchestration tools such as Apache Airflow, Dagster, or Astronomer.
3. 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.
4. Cloud & MLOps: Strong understanding of AWS, Azure, containerisation, Kubernetes, DevOps automation, and end-to-end ML lifecycle practices.
5. Data handling: Proven ability to wrangle, process, integrate, and analyse large, heterogeneous datasets, ideally in drug discovery or biomedical contexts.
6. 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.
7. Production ML: Demonstrated success building, training, and deploying production-grade machine learning models in industry and/or academic research environments.

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.