16 days ago
Base Salary
$248k - $335k/yr
Responsibilities
- Own the target architecture for knowledge modeling, storage, connectivity, and serving across the Intuit Customer Success Knowledge Graph and agentic intelligence layer.
- Define patterns for GraphRAG, hybrid retrieval, context engineering, tool and MCP design, multi-agent orchestration, and memory.
- Set the semantic layer and metrics strategy for consistent business definitions across dashboards, data agents, and AI experiences.
- Establish governance for knowledge quality, freshness, lineage, provenance, access control, and responsible AI.
- Evaluate graph stores, vector stores, semantic-layer tools, agent frameworks, and evaluation tooling, including build-versus-buy decisions.
- Prototype reference implementations, write production-quality code, review designs and pull requests, and resolve difficult technical issues.
- Deliver short-term business use cases while advancing the long-term architecture and reducing technical debt.
- Define evaluation frameworks for knowledge coverage, retrieval quality, answer accuracy, groundedness, and agent reliability.
- Translate architectural decisions between business, engineering, data, and operations stakeholders.
- Mentor data scientists, technical data analysts, and engineers while building the knowledge engineering practice.
Requirements
- 15+ years of experience in data and knowledge engineering or AI and ML engineering, including significant technical lead or architect experience on large-scale platforms.
- Track record of architecting and shipping large-scale production data and knowledge systems, such as enterprise knowledge graphs, semantic platforms, or data platforms.
- Deep expertise in ontology and taxonomy design, entity resolution, graph data modeling, knowledge graph construction, and LLM-based extraction and enrichment.
- Hands-on experience building LLM-powered and agentic systems involving RAG, GraphRAG, hybrid retrieval, agent frameworks, context engineering, and agent evaluation.
- Deep knowledge of vector and graph databases, embedding and retrieval pipelines, model serving, cloud data platforms, and scale, latency, and cost tradeoffs.
- Experience defining semantic-layer and metrics strategies for consistent business definitions across BI and AI consumers.
- Experience designing data and knowledge governance covering lineage, provenance, quality, freshness, access control, and responsible AI.
- Strong programming skills with the ability to write and review production code.
- Pragmatic architectural judgment, strong communication, and the ability to influence without authority across business and engineering teams.
- Experience mentoring and technically guiding data scientists, data professionals, and engineers.
- Bachelor’s degree in Computer Science, Data Science, Statistics, Information Science, or a related field required; an MS or PhD is preferred.
- Preferred experience includes customer success or contact-center knowledge systems, natural-language-to-SQL or self-serve data agents, semantic web standards, LLM evaluation and observability tooling, regulated domains, and relevant publications, patents, open-source work, or conference talks.
Benefits
- Competitive compensation package with pay-for-performance rewards.
- May be eligible for a cash bonus, equity rewards, and benefits under applicable plans and programs.
- Position is based in Mountain View, with compensation varying by work location.
Categories
AI ApplicationsData Engineering
About Intuit
Intuit is a global technology platform that helps our customers and communities overcome their most important financial challenges. Serving millions of customers worldwide with TurboTax, QuickBooks, Credit Karma and Mailchimp, we believe that everyone should have the opportunity to prosper and we work tirelessly to find new, innovative ways to deliver on this belief. We encourage conversations on this page and will not delete comments that follow our terms of use. In order to keep this a safe community, the below posts may be removed: Repeated posts of the same content, spam or posts from fake accounts or profiles, offensive language or material, threats to others in the community, posts deliberately aimed to have a negative effect on the community or conversations.