Abbott is a global healthcare leader, creating breakthrough science to improve people’s health. We’re always looking towards the future, anticipating changes in medical science and technology**.**
Interested in applying your wealth of technical knowledge and experience towards an opportunity in the medical field and improve the lives of people?
In our new Technology Hub in Barcelona, you will join our purpose driven team to:
Drive innovation in health tech by developing scalable platforms that transform real-time biosensor data into meaningful insights.
Shape the future of digital health by building solutions that empower people to take control of their metabolic health.
Create engineering with a global impact by working on technology that reaches millions worldwide.
Advance accessibility and compatibility by ensuring our solutions integrate seamlessly across devices and ecosystems.
About the position
The AI Engineer at Abbott will accelerate proof-of-concepts (PoCs) across Diabetes Care products and internal enterprise solutions. Our focus is applying Generative AI, AI agents, and Machine Learning to improve experiences, decision-making, and efficiency—both in customer/product contexts and in internal processes (e.g., documentation, quality workflows, analytics, operational automation).
This role is AI-first: you’re expected to use AI tools in your daily work to speed up delivery while maintaining engineering rigor, traceability, and quality.
Responsibilities
Build end-to-end AI workflows: data → model/agent logic → evaluation → deployable prototype.
Develop AI agents that use tools (function calling, retrieval, routing, multi-step plans, state/memory, workflow orchestration).
Apply AI first principles: model behavior, limitations, grounding strategies, uncertainty handling, prompt injection awareness, and safe-by-design patterns.
Design and run evaluations: golden datasets, automated checks, prompt/agent regression tests, and human-in-the-loop review when needed.
Implement fine-tuning / adaptation workflows when appropriate (dataset prep, training runs via managed services, versioning, validation).
Build and compare ML approaches (baselines, feature pipelines, metrics, error analysis) and combine them with GenAI when useful.
Integrate PoCs into real systems via APIs/services, and instrument for monitoring (latency, cost, quality).
Produce clear demos and documentation so results translate into go/no-go decisions and scalable next steps.
