Staff Software Engineer, AI (Org & Governance)

Wand AI; España; Remote; EUR 90,000 - 150,000; Full time

Job summary

Wand is 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 role focuses on designing AI agents, knowledge graphs, and scalable pipelines, with autonomy and rigorous testing, collaborating with a world-class team spanning research and product.

Qualifications

• A track record of building AI systems with agentic workflows and production delivery.
• Experience building or working with knowledge graphs in real, shipped systems.
• Hands-on with LLM APIs and agent frameworks like LangChain or LangGraph.

Responsibilities

• Design and build AI and agentic systems that analyze data to catch policy violations and governance issues.
• Build agents and pipelines that use LLMs to reason over large data volumes.
• Architect knowledge graph systems modeling org structure for agents.
• Tackle fuzzy problems, propose design, defend it, and implement with autonomy.
• Apply engineering rigor: testing, evaluation, and iteration of agent performance.
• Collaborate with Org Intelligence team for governance insights in product surfaces.
• Contribute across the stack beyond AI/agent layer.

Skills

AI systems

Knowledge graphs

LLM reasoning

Graph databases

LangChain/LangGraph

Remote-friendly

Software architecture

Tools

Neo4j

Cypher

RDF/SPARQL

Pinecone

Weaviate

pgvector

Job description

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.

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.

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.