Lead AI & Data Engineer – APD Platform

• You will design, develop, test, deploy, and maintain production-quality data and AI engineering solutions for the

. This will include developing data pipelines, data products, analytical services, machine learning capabilities, and AI-enabled applications that meet business, scientific, quality, security, privacy, and regulatory requirements.

• You will collaborate with stakeholders to understand user needs, define technical requirements, assess implementation options, and deliver solutions that are scalable, maintainable, observable, and fit for purpose. You will contribute to platform architecture, engineering standards, reusable patterns, and technical decisions that support the continued evolution of APD Platform capabilities.
• You will prepare, integrate, transform, and manage data from structured and unstructured sources. You will implement data quality, validation, lineage, metadata, access control, and governance measures to help ensure that data is discoverable, reliable, secure, and appropriately managed.
• You will contribute to the development and operationalisation of AI and machine learning solutions, including model deployment, monitoring, evaluation, versioning, and lifecycle management. This may include working with large language models, retrieval-augmented generation, prompt engineering, AI agents, and other generative AI technologies.
• You will apply modern engineering and DevOps practices, including continuous integration and delivery, automated testing, infrastructure as code, containerisation, monitoring, alerting, and incident resolution. You will also contribute to technical documentation, peer reviews, knowledge sharing, and continuous improvement across the platform engineering community.
• This role requires strong technical leadership across the APD Platform Data & AI team. You will provide technical direction, guide solution design and architecture, and ensure that engineering decisions align with platform strategy, enterprise standards, and scientific and business priorities.
• You will lead by example through hands-on engineering, high-quality technical reviews, and clear decision-making. You will establish and promote reusable patterns, engineering standards, and good practices for data, AI, software delivery, security, reliability, and operational excellence.
• You will mentor engineers, facilitate constructive technical discussions, resolve complex technical challenges, and help teams make pragmatic trade-offs. You will influence architects, product managers, scientists, vendors, and other engineering teams building alignment and shared accountability for sustainable outcomes.

• You will have substantial experience in

, or a closely related discipline, with a track record of providing technical leadership and delivering production-quality solutions in a complex enterprise environment.

• You should have strong programming experience in

, data modelling, APIs, version control, automated testing, and software development practices.

• You will have experience designing and implementing data pipelines, data products, or data platforms using cloud technologies. Familiarity with one or more major cloud platforms and associated services for storage, compute, databases, analytics, machine learning, identity, and monitoring is expected.
• You should understand machine learning engineering and

principles, including model development workflows, deployment, monitoring, reproducibility, performance evaluation, and responsible lifecycle management. Experience developing or operationalising AI/ML solutions, including generative AI applications, LLM integration, retrieval-augmented generation, model evaluation or AI service deployment, would be valuable.

• You will be comfortable with engineering and DevOps practices such as

, infrastructure as code, containerisation, observability, and agile delivery. Experience with technologies such as Spark, Docker, Kubernetes, Terraform, Git-based workflows, or comparable tools would be beneficial.

• You will be able to communicate complex technical concepts clearly to both technical and non-technical audiences. Collaboration, structured problem-solving, stakeholder engagement, and the ability to work effectively across multidisciplinary and geographically distributed teams are essential.

• Experience in the

, or another highly regulated industry would be beneficial. Knowledge of R&D processes, scientific data, laboratory data, clinical development, real-world data, or regulated technology environments would also be valuable.

• Additional experience with data governance, privacy, information security, FAIR data principles, responsible AI, knowledge graphs, semantic data models, or scientific computing would be advantageous. Familiarity with platform engineering, enterprise architecture, product-oriented delivery, or self-service data and AI platforms would further support success in the role.
• Experience in following technologies is desirable: PowerBI, Startburst, Snowflake, AWS Glue, Terraform, LangChain, LangGraph, LangSmith, FastAPI, Pydantic, Pandas
• A degree or equivalent professional experience in