Principal AI Solutions Architect

Awardco is reimagining the workplace to be more rewarding, supportive, and fun for everyone. As one of the fastest-growing companies in the employee experience industry, our mission is to help employees love what they do, love where they work, and get recognized for their efforts—especially our own employees! And as winners of Glassdoor’s Best Places to Work, Best in Brightest in the Nation, and Great Place to Work, we do much more than talk the talk. Awardco is building a governed context system so that our people and, increasingly, our AI agents can trust the data they act on. As Principal AI Solutions Architect, you will own the technical design of that foundation: the hub-and-spoke master data model, the event-log flow into Snowflake, and the governed APIs that let AI agents access trusted data safely. Your first project is standing up a governed context system across Syncari, Snowflake, and Glean to deliver one trusted account and contact record and one certified definition behind our key metrics. Your broader mandate is to execute Phase 1 of our AI Operations plan, including a stateful multi-agent system that automates go-to-market workflows and helps close a significant pipeline gap. This is a hands-on, build-oriented role for an architect who is as comfortable designing survivorship rules across six systems as standing up production RAG and agent-to-agent workflows, and who will act as the technical anchor of a lean team and the architecture authority to our Software Advisory Board. What you will do: Design the hub-and-spoke data schema that establishes Syncari as the overlay mastering operational records across Salesforce, HubSpot, and the other primary source systems without disrupting the existing Snowflake pipelines. Map and codify match and survivorship rules across the six primary systems, so conflicting records resolve into one trusted golden account and contact record with clear precedence logic and confidence thresholds. Architect the event-log flow into Snowflake, so operational state and change history stream reliably into certified, governed data structures using Snowflake Cortex. Define how AI agents access both datasets, operational (Syncari) and analytical (Snowflake), through governed APIs and MCP endpoints, enforcing read/write boundaries and human-approval gates. Design a stateful, multi-agent AI model and agent-to-agent (A2A) workflows, including the six micro-agents (identity, enrichment, qualification, routing, drafting, and feedback) that automate the manual middleware between HubSpot and Salesforce. Architect the retrieval-augmented generation (RAG), Data Graph, GraphRAG layer so LLM responses are grounded in a well defined Ontology with trusted definitions and edges so live operational & process data for Glean and other downstream agents reason over certified truth instead of guessing. Architect semantic API to AI and Semantic Governance and version control so all company health metrics and processes such as ARR, Account Health, and SDR pipeline. Then, ensure it can be mapped to a trusted golden record to those definitions are unified for every team and tool shares one answer. Stand up stewardship queues and governed writeback, routing high-risk conflicts to named human owners and ensuring clean values flow back into source systems only after human approval. Partner with Data Engineers and Data Analysts to sequence and deliver the Phase 1 project roadmap, turning architecture into hands-on build work and instrumenting data-quality and KPI dashboards. Serve as the technical authority to the Software Advisory Board, documenting architecture decisions, presenting at the Month 3, 6, and 9 checkpoints, and providing the evidence base for the decision to scale to new data domains. What you will bring: 8+ years in data architecture, enterprise systems integration, or AI/ML solutions architecture, including hands-on design of master data management (MDM) or enterprise integration solutions. Hands-on experience with a enterprise service bus (ESB) or a modern MDM platform, such as Syncari, Reltio, Boomi, or MuleSoft, including schema design and match/survivorship rule configuration. Proven, hands-on experience building LLM applications on commercial APIs (OpenAI, Anthropic) and designing production retrieval-augmented generation (RAG) architectures. Demonstrated ability to design multi-agent or agent-to-agent AI systems, including stateful orchestration and governed tool/API access patterns. Strong working knowledge of a cloud data warehouse (Snowflake preferred), including event and log ingestion and API-based data access. What will make you stand out: Direct experience with MindStudio, Syncari, Snowflake Cortex, Glean or similar platforms — or comparable MDM, semantic-layer, and AI search/orchestration platforms — in a governed-data setting. Understanding of Business Operational Systems systems (Salesforce, HubSpot, NetSuite) and customer or account data domains within a B2B SaaS company. Familiarity with the Model Context Protocol (MCP) or similar standards for governed AI agent access to enterprise data. Experience implementing data governance frameworks, including data contracts, stewardship workflows, and quality/KPI instrumentation. Awareness of AI regulatory and compliance