Lead AI & Data Engineer – APD Platform

Overview

In this Lead AI & Data Engineer role, you will shape the APD Platform, enabling R&D teams to access and trust data for advanced analytics and AI use cases. You’ll lead hands-on engineering while collaborating with data scientists, software engineers, and stakeholders to deliver secure, scalable data and AI capabilities. You will drive reusable patterns, platform standards, and governance to support data-driven decision-making across the organization.

This is a chance to influence how AI accelerates drug development in a collaborative, innovation-driven environment.

• in-person collaboration ~3 days/week
• flexible working arrangements
• inclusive and diverse environment
• opportunity to contribute to meaningful medicines
• collaboration with global experts

• Design, develop, test, deploy, and maintain production-grade data and AI solutions for the APD Platform
• Collaborate with stakeholders to translate needs into scalable, maintainable, and observable solutions
• Contribute to platform architecture, standards, and reusable patterns
• Ingest and manage data from structured and unstructured sources with quality controls and governance
• Develop and operationalize AI/ML capabilities, including model deployment and lifecycle management
• Apply DevOps practices: CI/CD, infrastructure as code, containerisation, monitoring, and incident response
• Create technical documentation, perform peer reviews, and promote knowledge sharing
• Provide technical leadership through hands-on work, reviews, and collaborative decision-making
• Mentor engineers and align engineering decisions with platform strategy and enterprise standards

• Extensive experience in data/AI/software engineering with technical leadership in a complex enterprise
• Strong programming in Python (and/or another modern language); solid SQL, data modelling, APIs, version control, and automated testing
• Experience designing and implementing data pipelines, data products, or data platforms in the cloud
• Familiarity with one or more cloud platforms and services for storage, compute, databases, analytics, ML, identity, and monitoring
• Understanding of ML engineering and MLOps, including deployment, monitoring, reproducibility, and lifecycle management
• Experience deploying AI/ML solutions, including generative AI, LLM integration, retrieval-augmented generation, and AI service deployment
• CI/CD, infrastructure as code, containerisation, observability, agile delivery
• Experience with Spark, Docker, Kubernetes, Terraform, Git workflows; strong communication across technical and non-technical audiences
• Collaborative, structured problem-solving, stakeholder engagement across distributed teams

• collaboration
• structured problem-solving
• stakeholder engagement
• Python
• SQL
• data pipelines