> Markdown version of [/jobs/ext/4544929-senior-ai-ml-data-model-architect](https://www.wearedevelopers.com/jobs/ext/4544929-senior-ai-ml-data-model-architect). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior AI/ML Data Model Architect - **Company:** Sapience AI Corporation - **Location:** Portland, OR, United States - **Experience:** Expert - **Salary:** $204,000.0 - $216,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Architecture, Data Infrastructure, Data Systems, Query Languages, Graph Database, Python (Programming Language), Operational Databases, Cloud Services, SQL Databases, Data Streaming, Knowledge Representation, Data Lineage, Machine Learning Operations - **Published:** November 3, 2026 - **Apply:** https://www.thejobnetwork.com/job/a43dcf93-e201-43e2-80c2-d9bf38d83593/senior-aiml-data-model-architect ## About the Role Who you areRequired qualifications * Seven or more years in data architecture, data modeling, or a closely related field. * Experience designing data models and representations for ML or AI systems. * Strong grounding in schema design, data modeling, and knowledge representation. * Experience with features, embeddings, and representations for models and retrieval. * Strong understanding of data lineage, quality, and governance. * Fluency across relational, graph, and vector data paradigms. * The ability to align many teams around a shared foundation., * Experience with knowledge graphs and neuro-symbolic systems. * Experience architecting data for retrieval-augmented and reasoning systems. * Experience with privacy, governance, and sensitive data by design. * Familiarity with CRM, AMS, or knowledge-platform data. * Experience carrying a data architecture through significant growth. How you work * You name the real data problem before reaching for a model. * You design for coherence and resist local fragmentation. * You treat quality, lineage, and trust as structural. * You align teams around a shared foundation. * You design for change so growth does not mean rework. Skills & Competencies * Data modeling and schema architecture for AI. * Representations for models, retrieval, and graphs. * Data flow, lineage, and provenance. * Consistency, quality, and correctness by design. * Privacy, governance, and trust in data architecture. * Cross-team alignment on shared foundations. * Evolving architecture for scale and change. Services & Tools Experience * Relational, graph, and vector data systems. * Data modeling and schema tooling. * Feature and embedding stores. * Data lineage, catalog, and governance tools. * SQL, Python, and graph query languages. * Cloud data platforms and storage. * Architecting the data behind the KO graph, COGENT architecture, and MINERVA. Prior Experience & Background * Prior data architecture or data modeling roles for ML or AI systems. * Experience owning a data foundation used by many teams. * A track record of coherent, scalable data architecture. * Experience with graph and vector data is strongly preferred. Cross-functional partners You work most closely with Knowledge Graph Engineering, Applied AI, ML Infrastructure, Neuro-Symbolic AI, and Research, and you partner with the Principal AI Systems Architect. You own the data models and representations behind the KO graph, the COGENT architecture, and MINERVA. ## Description The Senior AI/ML Data Model Architect owns that foundation. You design the data models and representations that keep the platform coherent and let every AI system built on them work well. What you will own (Areas of Responsibility) You hold seven areas of responsibility across the data and model layer. Each one is yours to set direction on and be accountable for. 1. Data modeling and schema architecture * Design the data models and schemas that represent a community's knowledge for AI. * Keep the models coherent across communities and across the systems that use them. * Balance flexibility with the structure that reasoning and trust require. 2. Representations for models and reasoning * Design how knowledge is represented for models, retrieval, and the KO graph. * Own the feature and embedding representations that AI systems depend on. * Align representations so neural and symbolic systems can work together. 3. Data flow and lineage * Architect how data flows from raw sources to models, the graph, and reasoning. * Own lineage and provenance so data can be traced and trusted. * Make the flow observable and reliable. 4. Consistency and quality by design * Set the standards that keep data consistent and high quality across the platform. * Prevent fragmentation and duplicated, divergent models. * Make correctness a property of the design. 5. Governance, privacy, and trust * Design data models that protect sensitive community knowledge and support governance. * Build privacy and access into the structure of the data. * Support the trust members and organizations place in the platform. 6. Partnership across data and AI teams * Partner with knowledge graph, applied AI, infrastructure, and research on shared representations. * Align teams around a common data foundation rather than local models. * Resolve the hard cross-team data questions. 7. Evolution and scale * Evolve the data models as the platform, communities, and AI systems grow. * Design for scale and for change without constant rework. * Keep the foundation coherent through each GR/GA milestone. AI-augmented ways of working You architect the data that AI depends on, and you use AI to do it, to explore schema options, reason about representations, and move faster, while you own the coherence, quality, and trustworthiness of the design. The standard is human in partnership: AI accelerates the work, you own the judgment, the interpretation, and the call. The people who create the most value here are not the ones producing the most output. They are the ones turning evidence into clear, durable decisions. What this role is not To keep the boundary clear: * This is not a pipeline-implementation-only role. You own the architecture and models, partnering with engineers who build the pipelines. * This is not a knowledge-graph-only role. You own data models across models, retrieval, and the graph, in partnership with graph engineering. * This is not a business-analytics role. You architect data for AI systems, not reporting and dashboards. * This is not a research role. You are accountable for a production data foundation, not open-ended exploration.