Lead AI & Data Engineer – APD Platform
AstraZeneca1 day ago
Barcelona, SpainStaff+
Responsibilities
- Design, develop, test, deploy, and maintain production-quality data and AI engineering solutions for the APD Platform.
- Build data pipelines, data products, analytical services, machine learning capabilities, and AI-enabled applications.
- Prepare, integrate, transform, and manage structured and unstructured data with quality, validation, lineage, metadata, access-control, and governance measures.
- Operationalize AI and machine learning solutions through deployment, monitoring, evaluation, versioning, and lifecycle management, including generative AI use cases.
- Apply engineering and DevOps practices including automated testing, infrastructure as code, containerization, monitoring, alerting, and incident resolution.
- Contribute to platform architecture, reusable patterns, engineering standards, technical documentation, peer reviews, and technical decisions.
- Provide hands-on technical leadership, mentor engineers, facilitate technical discussions, resolve complex challenges, and align multidisciplinary stakeholders.
Requirements
- Substantial experience in data engineering, AI engineering, software engineering, or a closely related discipline, with a record of technical leadership and production delivery in complex enterprise environments.
- Strong programming experience in Python and/or another modern programming language, plus SQL, data modeling, APIs, version control, automated testing, and software development practices.
- Experience designing and implementing cloud-based data pipelines, data products, or data platforms, with familiarity with major cloud platforms and services for storage, compute, databases, analytics, machine learning, identity, and monitoring.
- Understanding of machine learning engineering and MLOps principles, including deployment, monitoring, reproducibility, performance evaluation, and responsible lifecycle management.
- Experience or familiarity with generative AI applications, LLM integration, retrieval-augmented generation, model evaluation, or AI service deployment is valuable.
- Comfort with CI/CD, infrastructure as code, containerization, observability, and agile delivery; experience with Spark, Docker, Kubernetes, Terraform, or Git-based workflows is beneficial.
- Clear communication, structured problem-solving, stakeholder engagement, and effective collaboration across multidisciplinary and geographically distributed teams.
- Experience in pharmaceutical, biotechnology, healthcare, life sciences, regulated technology, R&D, scientific data, laboratory data, clinical development, or real-world data is desirable.
- Additional desirable knowledge includes data governance, privacy, information security, FAIR data principles, responsible AI, knowledge graphs, semantic data models, scientific computing, platform engineering, enterprise architecture, and self-service data and AI platforms.
- A degree or equivalent professional experience in computer science, engineering, data science, mathematics, life sciences, or a related subject is preferred.
Benefits
- Hybrid working with an average minimum of three days per week in the office in Barcelona, balanced with individual flexibility.
- Opportunity to work on meaningful technical and scientific challenges supporting medicine discovery and development.
- Inclusive environment with collaboration across disciplines and opportunities to contribute, develop, and thrive.
Tech Stack
Categories
Data EngineeringML Engineering
About AstraZeneca
AstraZeneca is a global biopharmaceutical company that discovers, develops, manufactures, and commercializes prescription medicines for patients and healthcare systems. Its portfolio spans oncology, cardiovascular, renal and metabolism, respiratory and immunology, and rare diseases (expanded by the 2021 acquisition of Alexion). Founded in 1999 and headquartered in Cambridge, UK, it is a public company listed on the London Stock Exchange and Nasdaq, generating revenue primarily from drug sales and partnerships.