Lead AI & Data Engineer – APD Platform

Description

The company's early science labs run complex drug projects, with scientists experimenting across many data types, workflows and vendors.

The APD platform adds digital capability to two business areas, complex synthetic modalities and biologics, which together cover chemistry, manufacturing and controls (CMC) for new medicines.

This senior individual contributor role sits in R&D IT, in the APD Platform Data & AI team in Barcelona. You will give hands-on technical leadership on scalable data and AI capabilities that help R&D teams access, manage, analyse and gain value from trusted data.

You will work alongside data and software engineers, AI specialists, architects, product managers, scientists and business stakeholders to build secure, reliable, reusable platform features for data-driven decisions, analytics, machine learning and new AI uses across R&D.

Success means reliable, secure, reusable data and AI capabilities that R&D teams adopt and that deliver measurable value. You will improve the access, quality, governance and usability of R&D data, enable secure and scalable AI, strengthen engineering standards, speed up delivery through automation and reuse, and build trust with stakeholders.

Responsibilities

• Build, test, ship and run production-grade data and AI solutions for the platform, including pipelines, data products, analytics services, ML features and AI applications that meet business, scientific, quality, security, privacy and regulatory needs.
• Work with stakeholders to understand user needs, set technical requirements, compare implementation options and deliver scalable, maintainable, observable solutions. Shape platform architecture, standards, reusable patterns and technical choices.
• Ingest, integrate and transform structured and unstructured data. Put quality checks, validation, lineage, metadata, access control and governance in place so data is findable, reliable, secure and well managed.
• Help build and run AI and ML solutions, covering deployment, monitoring, evaluation, versioning and lifecycle management. Work may involve LLMs, retrieval-augmented generation, prompt engineering, AI agents and other generative AI.
• Apply modern engineering and DevOps practice: CI/CD, automated testing, infrastructure as code, containers, monitoring, alerting and incident response. Contribute documentation, peer reviews, knowledge sharing and continuous improvement.
• Give technical direction across the APD Platform Data & AI team, guide solution design and architecture, and keep engineering choices aligned with platform strategy, enterprise standards and scientific and business priorities.
• Lead by example through hands-on work, strong technical reviews and clear decisions. Set and promote reusable patterns, standards and good practice for data, AI, delivery, security, reliability and operations.
• Mentor engineers, lead technical discussions, solve hard problems and help teams make pragmatic trade-offs. Influence architects, product managers, scientists, vendors and other engineering teams toward shared ownership of lasting outcomes.

Requirements

• Substantial experience in data engineering, AI engineering, software engineering or a closely related field, including technical leadership and delivering production solutions in a complex enterprise setting.
• Strong Python and/or another modern programming language, plus solid SQL, data modelling, APIs, version control, automated testing and software development practice.
• Experience designing and building data pipelines, data products or data platforms on cloud technologies. Familiarity with at least one major cloud platform and its storage, compute, database, analytics, machine learning, identity and monitoring services.
• Understanding of ML engineering and MLOps, including model development workflows, deployment, monitoring, reproducibility, performance evaluation and responsible lifecycle management.
• Comfort with CI/CD, infrastructure as code, containerisation, observability and agile delivery.
• Ability to explain complex technical concepts to technical and non-technical audiences. Collaboration, structured problem-solving, stakeholder engagement and working across multidisciplinary, geographically distributed teams are essential.
• Desirable: experience developing or operationalising AI and ML solutions, such as generative AI applications, LLM integration, retrieval-augmented generation, model evaluation or AI service deployment.
• Desirable: experience with tools such as Spark, Docker, Kubernetes, Terraform, Git-based workflows or comparable technologies.
• Desirable: experience in pharmaceuticals, biotechnology, healthcare, life sciences or another highly regulated industry, or knowledge of R&D processes, scientific data, laboratory data, clinical development, real-world data or regulated technology environments.
• Desirable: experience with data governance, privacy, information security, FAIR data principles, responsible AI, knowledge graphs, semantic data models or scientific computing, or with platform engineering, enterprise architecture, product-oriented delivery or self-service data and AI platforms.
• Desirable: experience with PowerBI, Startburst, Snowflake, AWS Glue, Terraform, LangChain, LangGraph, LangSmith, FastAPI, Pydantic or Pandas.
• Desirable: a degree or equivalent professional experience in computer science, engineering, data science, mathematics, life sciences or a related subject, which is preferred.

Conditions

Based in Barcelona.

Hybrid working: on average, a minimum of three days per week in the office, with room for individual flexibility.

Date posted 29-oct-2026. Closing date 03-dic-2026.

The company welcomes applications from all qualified candidates and follows applicable non-discrimination and work authorization laws.