About the position
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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). xhfqzwm
Strong communication skills: can explain results, risks, and tradeoffs to technical and non‑technical stakeholders.
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