AI Solution Architect

Responsibilities

• Translate business problems into solution designs and technical specs — choosing the right approach (including simpler non-AI options) for the problem.
• Architect end-to-end AI/ML solutions: data access and pipelines, model integration or development, retrieval/knowledge layers, agents, and application integration. Applied ML (e.g. vision, NLP, OCR) and GenAI both fall in scope.
• Define reusable architecture patterns, standards, and the review process that keeps team-built and BU-built solutions aligned and production-ready.
• Set and steward data-access, PII, and security guardrails for AI applications, in partnership with governance.
• Evaluate and recommend AI/ML technologies; own build-vs-buy-vs-fine-tune-vs-enable decisions, and establish evaluation, observability, and cost practices for production.
• Provide technical leadership across the solution lifecycle and partner with Data Engineering, Data Science, Data Platform, Data Product, BU builders / AI Champions, and Platform/IT.

Requirements

• Experience as a Solution Architect and/or System Analyst delivering production AI/ML or data products (retail a plus).
• Breadth across applied ML and GenAI application architecture — LLMs, RAG, agents, prompt engineering, and evaluation — with the judgment to mix pre-trained, fine-tuned, and custom models as the problem warrants.
• Solid data engineering and cloud platform foundations (GCP is a plus).
• Hands-on with AI/ML frameworks and APIs — agent/orchestration frameworks, vector stores, and model APIs/MCP.
• Working knowledge of data governance, PII handling, security, and responsible-AI principles — and how to operationalize them.