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.