Lead AI & Data Engineer – APD Platform

About the job

About the role

Are you ready to power the business to have a bigger impact on patients' lives? Embrace the novel opportunity to work at a business that puts patients first. In Early Science, we have highly skilled scientists in our labs who experiment and support complex drug projects. The environment is driven by scientific and technical innovation with a high level of diversity in workflows, data, and vendors. The Accelerated Pharmaceutical Development (APD) platform drives digitization in two business areas, cutting across both complex synthetic modalities & biologics, that combined deliver AstraZeneca’s chemistry, manufacturing, and controls (CMC) which are crucial activities when developing new pharmaceutical products.

AstraZeneca’s R&D IT organisation is seeking a Lead AI & Data Engineer to join the APD Platform Data & AI team in Barcelona. This senior individual-contributor role will provide hands-on technical leadership while supporting the design, development, and operation of scalable data and artificial intelligence capabilities that enable research and development teams to access, manage, analyse, and derive value from trusted data.

As part of the APD Platform, you will work with data engineers, AI specialists, software engineers, architects, product managers, scientists, and business stakeholders to deliver secure, reliable, and reusable platform capabilities. You will contribute to solutions that support data-driven decision-making, advanced analytics, machine learning, and emerging AI use cases across the R&D organisation.

Key responsibilities

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

Essential experience and skills

• You will have substantial experience in data engineering, AI engineering, software engineering, 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 Python and/or another modern programming language, together with solid knowledge of SQL, 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 MLOps 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 CI/CD, 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.

Desirable experience

• Experience in the pharmaceutical, biotechnology, healthcare, life sciences, 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 computer science, engineering, data science, mathematics, life sciences, or a related subject is preferred.

What success looks like

Success in this role will mean delivering reliable, secure, and reusable data and AI capabilities for the APD Platform that are adopted by R&D teams and provide measurable value.

You will help improve the accessibility, quality, governance, and usability of R&D data while enabling the secure and scalable use of AI. You will also help strengthen engineering standards, accelerate delivery through automation and reusable patterns, and build trusted relationships with scientific, business, and technology stakeholders.

Why AstraZeneca