Partner with product owners to define AI use cases, user journeys, feasibility, business outcomes and acceptance criteria. Challenge when conventional software or analytics is a better fit than AI.
Lead end-to-end AI product architecture across experience, application, model, data, integration and infrastructure layers. Document decisions, trade-offs, dependencies and non-functional requirements.
Design appropriate solutions using predictive ML, generative AI, retrieval-augmented generation (RAG), multimodal models and agentic workflows. Use autonomous or multi-agent designs only where they add value.
Responsibilities
Partner with product owners to define AI use cases, user journeys, feasibility, business outcomes and acceptance criteria. Challenge when conventional software or analytics is a better fit than AI.
Lead end-to-end AI product architecture across experience, application, model, data, integration and infrastructure layers. Document decisions, trade-offs, dependencies and non-functional requirements.
Design appropriate solutions using predictive ML, generative AI, retrieval-augmented generation (RAG), multimodal models and agentic workflows. Use autonomous or multi-agent designs only where they add value.
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