wand-ai - Staff Software Engineer, AI (Org & Governance)
Requirements
• A track record of building AI systems as a creator, not a consumer. You can talk in real depth about an agentic workflow, model, or system you built and shipped, not just a tool you use day to day. • Experience building or working with knowledge graphs in a real, shipped system. • Experience building agents or LLM based systems that reason over unstructured or ambiguous data, not simple deterministic pipelines. • Comfortable owning a project from architecture to delivery with real autonomy: you propose the design, defend it, then build it. • Strong software engineering fundamentals and the independence to thrive in a fast moving, remote first, senior-heavy team. • A history of turning fuzzy, non-trivial problems into concrete, working systems. • Practical experience with knowledge graph tooling: graph databases and query languages such as Neo4j/Cypher, RDF/SPARQL, Amazon Neptune, or similar. • Hands on experience with LLM provider APIs (OpenAI, Anthropic, or similar) and agent frameworks such as LangChain or LangGraph. • Comfortable with retrieval infrastructure like vector databases (Pinecone, Weaviate, pgvector, or similar) for grounding agent reasoning in organizational data. • Strong in a language well suited to graph construction, agent pipelines, and data analysis • Experience building security, risk, or compliance related tooling, especially anything involving policy or violation detection. • Prior experience at a company building agentic or AI-native products, rather than bolting AI onto an existing product as a feature. • Comfortable working fully remote, across time zones, on a small and highly autonomous team.
Responsibilities
• Design and build AI and agentic systems that analyze organizational data to catch policy violations, compliance risks, and governance issues, often from ambiguous, non-deterministic signals. • Build agents and pipelines that use LLMs to reason over large volumes of data, going well beyond deterministic, rule based checks. • Architect and build knowledge graph systems that model organizational structure and relationships, and make that context usable by agents. • Take a fuzzy, undefined problem ("find policy violations across messy data"), propose a real technical approach, defend it, then build it with minimal oversight. • Bring engineering rigor to inherently fuzzy territory: testing, evaluating, and iterating on how well your agents actually perform. • Partner with the rest of the Org Intelligence team to get AI-driven insight into the product's governance and compliance surfaces. • Contribute across the stack when it helps, though the core of this role is the AI and agent layer, not the UI.
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