About Abbott
Si le interesa solicitar este empleo, por favor, asegúrese de cumplir los siguientes requisitos que se enumeran a continuación.
Abbott is a global healthcare leader, creating breakthrough science to improve people’s health. We’re always looking toward the future, anticipating changes in medical science and technology.
Job Description
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 digital health solutions that empower people to take control of their metabolic health;
create engineering witha global impact by working on technology that reaches millions worldwide;
and 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 on applying Generative AI, AI agents, and machine learning to improve experiences, decision‑making, and efficiency in both customer/product contexts and in internal processes such as documentation, quality workflows, analytics, and operational automation. This role is AI‑first;
you’re expected to use AI tools daily to speed 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 such as function calling, retrieval, routing, multi‑step plans, state/memory, and 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 using golden datasets, automated checks, prompt/agent regression tests, and human‑in‑the‑loop review when needed.
Implement fine‑tuning or adaptation workflows when appropriate, including dataset preparation, managed‑service training runs, versioning, and validation.
Build and compare ML approaches, including baselines, feature pipelines, metrics, error analysis, and combine them with Generative AI when useful.
Integrate PoCs into real systems via APIs/services, and instrument for monitoring latency, cost, and 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, and ability to ship prototypes quickly.
Hands‑on Generative AI/LLM experience using cloud APIs and delivering solutions beyond notebooks.
Proven experience building AI workflows and agents that use tools such as orchestration, routing, structured outputs, and state handling.
Solid understanding of AI first principles, including why models fail, hallucinations, grounding, trade‑offs, and evaluation‑driven development.
Experience with evaluation and testing for AI systems, including unit/integration tests and model‑quality evaluation.
Experience with fine‑tuning or model adaptation workflows and knowing when not to fine‑tune.
Strong machine‑learning fundamentals: data preparation, training/inference, metrics, baseline comparisons, and model selection. xhfqzwm
Strong communication skills: ability to explain results, risks, and trade‑offs to technical and non‑technical stakeholders.
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or continue with email
Si le interesa solicitar este empleo, por favor, asegúrese de cumplir los siguientes requisitos que se enumeran a continuación.
Abbott is a global healthcare leader, creating breakthrough science to improve people’s health. We’re always looking toward the future, anticipating changes in medical science and technology.
Job Description
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 digital health solutions that empower people to take control of their metabolic health;
create engineering witha global impact by working on technology that reaches millions worldwide;
and 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 on applying Generative AI, AI agents, and machine learning to improve experiences, decision‑making, and efficiency in both customer/product contexts and in internal processes such as documentation, quality workflows, analytics, and operational automation. This role is AI‑first;
you’re expected to use AI tools daily to speed 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 such as function calling, retrieval, routing, multi‑step plans, state/memory, and 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 using golden datasets, automated checks, prompt/agent regression tests, and human‑in‑the‑loop review when needed.
Implement fine‑tuning or adaptation workflows when appropriate, including dataset preparation, managed‑service training runs, versioning, and validation.
Build and compare ML approaches, including baselines, feature pipelines, metrics, error analysis, and combine them with Generative AI when useful.
Integrate PoCs into real systems via APIs/services, and instrument for monitoring latency, cost, and 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, and ability to ship prototypes quickly.
Hands‑on Generative AI/LLM experience using cloud APIs and delivering solutions beyond notebooks.
Proven experience building AI workflows and agents that use tools such as orchestration, routing, structured outputs, and state handling.
Solid understanding of AI first principles, including why models fail, hallucinations, grounding, trade‑offs, and evaluation‑driven development.
Experience with evaluation and testing for AI systems, including unit/integration tests and model‑quality evaluation.
Experience with fine‑tuning or model adaptation workflows and knowing when not to fine‑tune.
Strong machine‑learning fundamentals: data preparation, training/inference, metrics, baseline comparisons, and model selection. xhfqzwm
Strong communication skills: ability to explain results, risks, and trade‑offs to technical and non‑technical stakeholders.
#J-18808-Ljbffr
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