Consultant, AI Engineer (Applied​/Software), IT​/Tech

Experience : 6 to 9 Years

We are looking for creative problem solvers with a passion for tackling tough customer problems involving data to serve as a Knowledge Enablement Engineer, with responsibility for evaluating, piloting, and then scaling the capability that makes our data findable, trustworthy, and usable — by people and by AI agents. If you're passionate about large initiatives at a scale that helps transform the lives of internal and external stakeholders, this is that kind of work.

Intuit's Global Business Solutions Group builds tools and services that help small and mid-sized businesses manage cash flow and grow. Within this mission, our Data Science team is building the knowledge layer between our data and the people and agents who use it: canonical metric definitions, semantics, data dictionaries, and the surfaces that make them discoverable.

This role sits at the intersection of semantic modeling and AI enablement. We are looking for a proactive, end-to-end contributor: someone who can evaluate existing capabilities on their merits, stand up a working pilot rather than an assessment, make a clear recommendation, and carry it into adoption. Programs for the coming year involve everything from evaluating existing tools across semantics, context, and knowledge graphs, to piloting the strongest option and determining how our current semantic layer feeds into it, to scaling the result across

Evaluate existing tools and platforms across semantics, context, and knowledge graphs, and assess our requirements against them

Pilot the strongest option, and determine how our existing semantic layer serves as an input to it

Deliver a working pilot together with a clear recommendation

Onboard domains onto the approach we adopt, expanding content coverage as you go

Support alignment sessions so that definitions are agreed before they are codified

Create playbooks and establish best practices so each domain onboards more easily than the last

Author the AI rules, skills, and files that make the layer usable by both people and agents

Develop tool-agnostic capabilities that serve multiple AI tools rather than a single vendor

Integrate through code or configuration depending on the adoption path, and evaluate graph-based approaches as a later component

Demonstrated depth building semantic layers, knowledge graphs, retrieval systems, or agent tooling.

A proactive, end-to-end approach, and comfort operating with a high degree of autonomy

SQL and Python sufficient to work on a data science team; SQL forms part of the content itself

Experience building with modern AI tooling; transferable experience matters more to us than any specific vendor

Demonstrated ability to build strong partnerships across teams, including outside your own organization

Technical education or equivalent work experience

Focus strategically. Has autonomy to work collaboratively with the leaders in your space to drive the evaluation and rollout. Makes and defends build-versus-adopt judgments — a well-reasoned recommendation to reuse an existing capability is as valuable to us as a recommendation to build a new one.

Deliver. Produces a working pilot rather than an assessment, and sequences the rollout so that each domain onboarded is a durable gain. Uses independent judgment to provide insights, and communicates progress, trade-offs, and risks to stakeholders. Collaborates closely with the hiring manager on review of design and requirements.

Experience with semantic layers or specifications such as Metric Flow or Open Semantic Interchange

Experience with MCP servers, agent orchestration, or retrieval systems

Experience driving adoption of a platform capability across multiple teams

SQL and Python sufficient to work on a data science team; SQL forms part of the content itself

Experience building with modern AI tooling; transferable experience matters more to us than any specific vendor