Principal Software Engineer (AI)Our work focuses on delivering innovative technology that creates meaningful, real-world impact. We are united by a shared commitment to developing solutions that help organisations harness the power of data, AI, and advanced software capabilities.We help life sciences and BioPharma organisations unlock the potential of AI and machine learning to improve outcomes and accelerate innovation. Through cloud-based software platforms and specialist services, we support organisations in managing complex scientific and operational data, improving workflows, and enabling smarter decision-making.With decades of expertise in scientific informatics, our solutions help organisations design, execute, and optimise processes, manage and structure data, and generate valuable insights across the product lifecycle—from research and development through to manufacturing.The Principal Software Engineer (AI) will provide technical leadership in the design and delivery of production-grade, GenAI-enabled capabilities across enterprise software platforms. The role focuses on applying large language models (LLMs) to real-world enterprise data, including scientific, clinical, and operational information, to enable AI-driven discoverability, summarisation, reporting, and decision support.Operating at principal level, you will be hands-on, guiding engineering teams through the transition from proof of concept (POC) to early adoption and general release, while helping establish sustainable AI engineering practices across the wider technology organisation.You will collaborate closely with data scientists, domain specialists, and technical teams to deliver intelligent solutions for knowledge extraction, decision support, and workflow automation.In this role, you will have the opportunity to:Lead the delivery of AI-enabled platform capabilities that accelerate insight generation and decision-making across complex enterprise environments.Design and productionise scalable GenAI solutions using approaches such as retrieval augmented generation (RAG), integration frameworks, and selectively applied agentic patterns—balancing capability, cost, performance, and trust.Translate scientific and business requirements into robust AI system designs that integrate effectively with data platforms, workflows, and APIs.Establish engineering best practices for GenAI and LLM applications, including architecture patterns, evaluation approaches, reliability, scalability, and maintainability standards across the AI engineering lifecycle.Act as a technical leader by mentoring engineers, influencing architectural decisions, and supporting successful delivery and adoption of AI capabilities across software platforms.Essential requirements:Experience designing, building, and operating LLM-powered capabilities such as document summarisation, retrieval augmented generation (RAG), and question-answering systems over enterprise datasets.Experience developing AI-ready data foundations, including unstructured data pipelines, metadata extraction and enrichment, and hybrid architectures using vector stores alongside relational databases.Proven ability to deploy AI systems into production environments, including monitoring model behaviour, data drift, cost, and latency.Strong judgment in making pragmatic engineering trade-offs to support reliability, scalability, and enterprise adoption.Experience establishing robust AI operational practices, including versioning of prompts, models, and data, as well as implementing feature flags and controlled rollout strategies.Ability to collaborate effectively with cross-functional partners, provide technical leadership, mentor engineers, and drive delivery in complex environments.Desirable (but not essential):Experience applying GenAI solutions within life sciences, healthcare, scientific, clinical, or regulated environments.Familiarity with building AI capabilities that support knowledge extraction, decision support, and workflow automation across enterprise data ecosystems.Experience with cloud-based platforms and modern data technologies such as Databricks and AWS.Experience defining or contributing to organisational best practices for AI engineering, including evaluation methods, governance frameworks, and scalable adoption approaches for generative and agentic AI.
Edward Killin
