Staff Software Engineer, AI (Org & Governance)
- Role
- AI / ML
- Experience
- Senior
- Employment
- Full-time
Open to Anywhere in Europe · UTC+0…UTC+3. Set where you work from to check your eligibility.
No BS summary
Senior engineer to design and build agentic AI systems and knowledge-graph-backed agent pipelines for governance and policy-violation detection. Must have shipped agentic/LLM-based systems and practical knowledge-graph experience (Neo4j/RDF/Neptune/etc.). Remote, working Europe timezone.
Core skills
Required skills
Optional skills
Build the Future Workforce
Wand turns AI into labor. It enables humans and AI agents to operate together as a unified, hybrid workforce, with comprehensive management and oversight. And it’s already operating at scale inside some of the world’s largest organizations.
Wand built the world’s first Agentic Labor Infrastructure enabling governments and global enterprises to create, manage, and scale digital workforces.
Our mission is to integrate agent ecosystems into the core of work and business, unlocking a generational leap in the global economy. We’re building the infrastructure that lets humans and AI agents operate together safely, transparently, and at scale.
Join Wand in leading the Agentic Shift
Wand is building a high-performing global team who take full ownership of what they build. We lead by example, move fast, make data-aware decisions, and continuously push for more- always with a focus on delivering real value to customers.
You would be joining a world-class team that combines deep research expertise and real-world product execution, with experience spanning Deepmind, Google, Amazon, Miro, Elise AI, IBM and Accern.
Position Summary
We're hiring a Staff Software Engineer to build the AI and agentic engines behind governance at Wand: systems that reason over messy, non-deterministic organizational data to catch policy violations, flag risk, and surface what humans actually need to see. This isn't about using AI day to day. It's about having built it: agentic workflows, models, or systems that went from idea to production.
You'll join the Org Intelligence team as one of its most senior technical voices, with the autonomy to propose an approach, defend it, and then go build it, largely on your own. We're a small, remote-first, high-trust team. There's no hand holding here and not much process either. You own the problem end to end.
Role 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.
Key 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
Preferred Experience
- 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.
What you'll do
- 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.
What they require
- 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
Benefits
- Remote-first, high-trust team
- Work with a team combining deep research expertise and product execution
- Autonomy and end-to-end ownership
Wand turns AI into labor. It enables humans and AI agents to operate together as a unified, hybrid workforce, with comprehensive management and oversight. Wand built the world’s first Agentic Labor Infrastructure enabling governments and global enterprises to create, manage, and scale digital workforces.