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.
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