Machine Learning Engineer

About the role Quotient Sciences is a drug development accelerator, helping to shorten timelines and bring new treatments to patients faster through our Translational Pharmaceutics® platform. As an AI Engineer, you will own the full AI lifecycle—from data ingestion through model development, deployment, and monitoring. You’ll build and maintain the technical foundations that enable delivery of AI products aligned with our strategic objectives. Recognised internally as a technical expert, you will ensure responsible AI practices, model governance, and compliance, while collaborating with product managers, data engineers, analysts, and business stakeholders to translate requirements into robust AI solutions. Main responsibilities Design, develop, and deploy AI and machine learning models to solve business problems and deliver measurable value. Test and select modelling approaches balancing performance, interpretability, and operational fit; tune/retrain models as needed. Build and maintain scalable ML pipelines and infrastructure for classical ML and deep learning. Deploy models to production using containerisation, CI/CD, and MLOps toolsets; manage ongoing configuration and administration. Develop LLM-based tools using prompt engineering, retrieval, and embedding pipelines for knowledge retrieval and workflow assistance. Build APIs, microservices, or workflow components to integrate AI tools into existing systems. Set up monitoring for model drift, performance, latency, and failures; maintain logging and observability standards. Embed responsible AI practices, governance, and compliance in all solutions; follow GxP and validation standards where required. Collaborate with cross-functional teams to translate business requirements into technical solutions. Produce clear documentation for models, pipelines, deployment steps, and operational expectations. Communicate complex technical concepts in clear, actionable terms to technical and non-technical stakeholders. Mentor and coach team members; foster a collaborative, high-performance culture. Stay current with advancements in AI/ML and data engineering; help shape common frameworks and best practices across the organisation. Skills required Demonstrable experience in AI engineering, machine learning, or data science roles. Proven track record of building, deploying, and maintaining production-grade AI models and pipelines. Strong proficiency in Python, R, and ML frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with cloud platforms and ML infrastructure (AWS SageMaker, MLflow). Practical understanding of monitoring, logging, and CI/CD. Experience with LLMs, vector search, or retrieval-augmented systems. Comfortable working with structured and unstructured data. Familiarity with responsible AI practices, data governance, and compliance frameworks. Applied knowledge of Agile principles (Kanban, Scrum) and roadmap delivery using tools like Jira. Excellent communication skills; able to explain complex concepts to non-technical audiences. Previous exposure to life sciences, biotech, or manufacturing desirable; awareness of CDMO processes and GxP/regulatory environments beneficial