AI Engineer Python

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 efficiencyboth in customer/product contexts and in internal processes (e.g. documentation quality workflows analytics operational automation).

This role is AI-first: youre 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.