Experteer Overview
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In this AI Engineer role at Abbott’s Barcelona Technology Hub, you will accelerate PoCs for Diabetes Care and enterprise solutions by applying Generative AI, AI agents, and ML. You’ll build end-to-end AI workflows, evaluate models, and integrate solutions into real systems to enable scalable health-tech insights. You’ll work in a fast-paced, AI-first environment prioritizing rigor, traceability, and quality. This is a gateway to shape digital health platforms that reach millions and improve metabolic health.
Compensaciones / Beneficios
• Build end-to-end AI workflows (data, model/agent logic, evaluation, deployable prototype)
• Develop AI agents utilizing tools (function calling, routing, multi-step plans, state/memory, orchestration)
• Apply AI-first principles: grounding, uncertainty handling, safe-by-design patterns
• Design and run evaluations with golden datasets, automated checks, and human-in-the-loop review
• Implement fine-tuning/adaptation workflows (dataset prep, training, versioning, validation)
• Compare ML approaches (baselines, feature pipelines, metrics, error analysis) and blend with GenAI when helpful
• Integrate PoCs into real systems via APIs/services and instrument monitoring (latency, cost, quality)
• Produce demos and documentation to support go/no-go decisions and scalable next steps
Responsabilidades
• Strong Python engineering, clean code, debugging, testing discipline, ability to ship prototypes quickly
• Hands-on GenAI/LLM experience using cloud APIs and delivering beyond notebooks
• Proven experience building AI workflows and agents that use tools (orchestration, routing, structured outputs, state handling) xqbhyrx
• Solid understanding of AI-first principles (model failure, hallucinations, grounding, 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 ML fundamentals (data prep, training/inference, metrics, baselines, model selection)
• Strong communication skills: explaining results, risks, and tradeoffs to technical and non-technical stakeholders
Requisitos principales
•
or continue with email
Si está considerando enviar una solicitud, asegúrese de pulsar el botón de solicitar de abajo después de leer la descripción completa.
In this AI Engineer role at Abbott’s Barcelona Technology Hub, you will accelerate PoCs for Diabetes Care and enterprise solutions by applying Generative AI, AI agents, and ML. You’ll build end-to-end AI workflows, evaluate models, and integrate solutions into real systems to enable scalable health-tech insights. You’ll work in a fast-paced, AI-first environment prioritizing rigor, traceability, and quality. This is a gateway to shape digital health platforms that reach millions and improve metabolic health.
Compensaciones / Beneficios
• Build end-to-end AI workflows (data, model/agent logic, evaluation, deployable prototype)
• Develop AI agents utilizing tools (function calling, routing, multi-step plans, state/memory, orchestration)
• Apply AI-first principles: grounding, uncertainty handling, safe-by-design patterns
• Design and run evaluations with golden datasets, automated checks, and human-in-the-loop review
• Implement fine-tuning/adaptation workflows (dataset prep, training, versioning, validation)
• Compare ML approaches (baselines, feature pipelines, metrics, error analysis) and blend with GenAI when helpful
• Integrate PoCs into real systems via APIs/services and instrument monitoring (latency, cost, quality)
• Produce demos and documentation to support go/no-go decisions and scalable next steps
Responsabilidades
• Strong Python engineering, clean code, debugging, testing discipline, ability to ship prototypes quickly
• Hands-on GenAI/LLM experience using cloud APIs and delivering beyond notebooks
• Proven experience building AI workflows and agents that use tools (orchestration, routing, structured outputs, state handling) xqbhyrx
• Solid understanding of AI-first principles (model failure, hallucinations, grounding, 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 ML fundamentals (data prep, training/inference, metrics, baselines, model selection)
• Strong communication skills: explaining results, risks, and tradeoffs to technical and non-technical stakeholders
Requisitos principales
•
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