📁 About our client:

Our client is the global leader in regulatory and sustainability intelligence, helping the world's largest companies navigate Environment, Health & Safety, Corporate Sustainability, and Product Compliance.

They are reinforcing their AI capabilities to keep their leadership as an AI-native compliance intelligence platform, with a foundational AI platform built around ontology, data, and platform surfaces, strong CAIO sponsorship, and direct executive air cover for the product layer of the AI transformation.


🚀 Responsibilities:

Knowledge Graph & Ontology Ownership

• Design, build, and own the regulatory knowledge graph on Neo4j: schema, Cypher query surface, indexing strategy, and the ontology extension model for client-specific overlays.

• Model typed entities, relationships, temporal amendment chains, and cross-jurisdictional linkage for EHS, Product, and ESG domains.

• Define and enforce ontology quality: drift detection, schema versioning, migration patterns, and the contract between graph and downstream consumers.

Inference & Reasoning

• Build and maintain inference rules and reasoning pipelines that turn graph structure into usable signal for applicability, gap analysis, and change detection.

• Carry strong opinions (loosely held) about where formal semantics earn their keep and where pragmatic shortcuts win.

Integration with AI Products

• Integrate the graph with retrieval, RAG pipelines, and agentic workflows. The ontology only counts if it shows up inside products.

• Partner with Applied ML and NLP engineers on entity linkage, embedding-based candidate generation, and graph-augmented retrieval.

• Prototype rapidly with AI coding assistants to validate modeling choices against real regulatory content before committing to production patterns.

Team & Technical Leadership

• Lead a small squad of ontology and knowledge graph engineers as a player-coach: roughly half of your time on hands-on build, half on direction-setting, review, and growing the people around you.

• Own hiring into the team and set the technical bar: code review culture, Cypher patterns, testing strategies, and deployment practices.
  
  

👤 Profile sought:

Experience:

• Clear history of shipping ontology or graph work into production with measurable business impact. Prototypes that never left the lab don't count.

• Track record building taxonomies and ontologies that survive contact with real content: typed entities, relationships, temporal modeling, inheritance, and extension patterns.

• Hands-on experience wiring graphs into LLM-powered retrieval, RAG pipelines, or agentic workflows, with a clear view of what graph-augmented retrieval buys you and where it doesn't.

Technical skills:

• Deep, hands-on experience with Neo4j and Cypher in production, modeling real-world domains (not toy examples), with the trade-offs and lessons learned to back it up.

• Strong Python skills for graph tooling, ingestion, and integration. Comfortable with at least one compiled or systems-level language when performance matters.

• Hands-on experience combining graph queries with vector search and full-text retrieval (Elasticsearch, Qdrant, or equivalents).

• Comfortable running Neo4j or equivalent graph infrastructure on Azure, AWS, or GCP. Familiar with containerization and CI/CD.

• Active user of AI coding assistants (Copilot, Claude Code, Cursor, etc.), integrated into daily workflow, with a clear point of view on what they are good and bad at, especially for schema exploration and Cypher generation.

Bonus:

• Familiarity with RDF/OWL, SHACL, or similar formalisms (pragmatism wins, but the background helps).

• Regulatory, legal, pharmaceutical, or healthcare domain experience, particularly with jurisdictional and temporal modeling.

• Experience with inference engines, reasoners, or rule systems (Datalog, SWRL, Drools, or similar).

• Published work, open-source contributions, or conference talks on knowledge graphs or ontology engineering.

• Background in NLP, entity linking, or information extraction, especially across unstructured legal or regulatory text.

• Experience designing client-extensible schemas or multi-tenant graph architectures.

Languages:

• Fluent English required.

Soft skills:

• Player-coach leadership style: technically credible, willing to ship Cypher and schema yourself, equally focused on growing the team.

• Opinionated about tools, pragmatic about deadlines.

• Strong communicator across technical and product audiences.

• Bias for delivery and operational discipline over research polish.

🌍 Benefits & Culture:

• Tech stack: Neo4j, Cypher, Python, Azure, RAG, vector search (Qdrant), Elasticsearch, LLM-powered retrieval, agentic workflows.

• Direct access to leadership and the Chief AI Officer: short feedback loops, real influence on architecture and roadmap.

• A seat on a small, high-impact AI team where the ontology layer is the company's defensible moat.

• Culture that treats AI tools as force multipliers, not novelties.

• Competitive compensation, benefits, and flexibility.

• Hybrid in Lisbon's Office role (3 days a week at the office)
  
  

💼 Department: AI & Engineering

📍 Location: Lisbon

📆 Start date: ASAP