type: role
slug: data-ai-engineer-architect
name: Data AI Engineer Architect
description: Owns AI analyst architecture across metadata, evaluation systems, and tool/prompt orchestration quality.
owner: dave
status: planned
Data AI Engineer Architect
About the Role
Dataface needs an engineer-architect who can make the AI analyst experience real, dependable, and extensible. This role owns the architecture behind the analyst AI system, including metadata contracts, tool interfaces, context assembly, prompt and skill orchestration, evaluation loops, and the operational patterns that make agents useful in real workflows instead of isolated demos.
This is a systems role as much as an AI role. The challenge is not just getting an LLM to do something impressive once. The challenge is building a product-quality analyst system that can reason over schema and business context, use tools correctly, recover from ambiguity, and improve through disciplined evaluation and iteration.
What You'll Do
- Define the architecture for the analyst AI stack across prompts, tools, metadata, memory and context strategies, evaluation, and runtime safety.
- Build the abstractions and interfaces that allow product teams to add new agent capabilities without creating brittle prompt sprawl.
- Design retrieval and context strategies that give the model the right information at the right time with strong cost, latency, and quality tradeoffs.
- Establish evaluation loops, benchmark suites, and review processes so model behavior can be measured, debugged, and improved systematically.
- Partner with product, engineering, and analyst-domain experts to translate real analytical workflows into robust AI-assisted product capabilities.
- Drive quality standards around correctness, trust, transparency, and failure handling in analyst-facing AI experiences.
What We're Looking For
- Deep experience building AI-enabled product systems, not just running experiments.
- Strong software architecture skills, especially around APIs, contracts, orchestration, and system boundaries.
- Practical understanding of LLM prompting, tool use, retrieval, structured outputs, and evaluation methods.
- Ability to work fluently across backend engineering, product constraints, and user workflow design.
- Clear judgment about where agent systems should be flexible and where they should be constrained.
- Comfort working in ambiguous environments where patterns are still being invented.
Why This Role Matters
The quality of the AI analyst experience will shape how differentiated Dataface feels. This role determines whether the system is merely novel or actually reliable enough to become part of an analyst's day-to-day workflow. Strong architecture here reduces product risk and creates leverage for the rest of the team.