Summary: Abbott is seeking an AI Engineer to drive innovation in health tech by developing scalable platforms and building solutions that empower people to take control of their metabolic health. Highlights: 1. Drive innovation in health tech with scalable platforms 2. Shape the future of digital health with impactful solutions 3. Create engineering with global impact for millions worldwide Job Description**About Abbott** 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. ### **Requirements** * **Strong Python engineering**: clean code, debugging, testing discipline, ability to ship prototypes quickly. * **Hands\-on GenAI/LLM experience** using cloud APIs and delivering solutions beyond notebooks. * Proven experience building **AI workflows and agents that use tools** (orchestration, routing, structured outputs, state handling). * Strong understanding of **AI first principles** (why models fail, hallucinations, grounding, tradeoffs, evaluation\-driven development). * Experience with **evaluation and testing** for AI systems (unit/integration tests \+ model\-quality evaluation). * Experience with **fine\-tuning or model adaptation** workflows (and knowing when *not* to fine\-tune). * Solid **machine learning fundamentals** (data prep, training/inference, metrics, baseline comparisons, model selection). * Strong communication skills: can explain results, risks, and tradeoffs to technical and non\-technical stakeholders.