AI Engineer - Python

Description

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 for meaningful insights 2. Shape the future of digital health by empowering metabolic health control 3. Create engineering with global impact, reaching 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.