Lead AI & Data Engineer – APD Platform

About the role

AstraZeneca's R&D IT organisation is looking for a Lead AI & Data Engineer to join the APD Platform Data & AI team in Barcelona. This senior individual-contributor role provides hands-on technical leadership while supporting the design, development and operation of scalable data and AI capabilities that let R&D teams access, manage, analyse and derive value from trusted data. The APD Platform drives digitisation across synthetic modalities and biologics, covering CMC activities for new pharmaceutical products.

Key responsibilities

• Design, develop, test, deploy and maintain production-quality data and AI solutions, including pipelines, data products, analytical services, machine learning capabilities and AI applications.
• Work with stakeholders to define technical requirements and deliver scalable, maintainable, observable solutions, and contribute to platform architecture, standards and reusable patterns.
• Prepare, integrate, transform and manage data from structured and unstructured sources, with quality, validation, lineage, metadata, access control and governance.
• Contribute to AI and ML solutions, including model deployment, monitoring, evaluation, versioning and lifecycle management, and possibly large language models, retrieval-augmented generation and AI agents.
• Apply CI/CD, infrastructure as code, containerisation, monitoring, alerting and incident resolution, and support documentation, peer reviews and knowledge sharing.
• Provide technical direction, guide solution design and architecture, mentor engineers and influence architects, product managers, scientists and vendors.

Required profile

• Substantial experience in data engineering, AI engineering, software engineering or a related discipline, with a track record of technical leadership in a complex enterprise environment.
• Strong programming in Python and/or another modern language, with SQL, data modelling, APIs, version control and automated testing.
• Ability to explain complex technical concepts to technical and non-technical audiences and to work across multidisciplinary, geographically distributed teams.
• Degree or equivalent professional experience in computer science, engineering, data science, mathematics, life sciences or a related subject preferred.

Required skills

• Cloud data platforms and services for storage, compute, databases, analytics, ML, identity and monitoring.
• MLOps principles and experience with generative AI, LLMs, RAG and AI service deployment is valuable.
• DevOps practices such as CI/CD, IaC, containerisation and observability; Spark, Docker, Kubernetes, Terraform and Git are beneficial.
• Desirable: experience in regulated industries, data governance, privacy, security, FAIR data, knowledge graphs, platform engineering, and tools such as PowerBI, Snowflake, AWS Glue, LangChain, FastAPI, Pydantic and Pandas.