Artificial Intelligence Engineer

What you will do 

• Design the knowledge representation: how constraints, transformations, procedures, and guidelines are modelled so that both agents and deterministic validators can use them, and so that humans can read, curate and correct them. 

• Build and operate platform services for ingesting, curating, retrieving, and versioning knowledge, ensuring it is scoped correctly to the relevant customer and workflow context. 

• Define how semantic retrieval, deterministic validation, and human review work together, including how applicability, uncertainty, conflicting evidence, and escalation are handled. 

• Design and implement reusable APIs and tools that allow AI systems and deterministic services to apply, validate, and explain contextual knowledge. 

• Build evaluation suites covering retrieval quality, applicability, conflict and ambiguity handling, and explanation quality, using measurable results as the primary evidence of progress. 

• Take end-to-end ownership of substantial technical areas, including their security, tenant isolation, reliability, observability, performance, and compatibility for consuming teams. 

• Work directly with domain experts and product engineering teams to turn informal expertise into structured, testable knowledge with clear provenance, citation, and release practices. 

What we are looking for 

• Strong software engineering experience designing, building, and operating production systems in Python, TypeScript, Java, or a similar language.  

• At least 8 years’ experience in a software engineering role, preferably including ownership of a platform, API or other shared-services product used across multiple internal teams.  

• Experience building knowledge, retrieval, or rule systems that other teams depend on, and the judgement to choose the right formalism for a problem, whether that is a schema, decision table, rules engine, graph, or retrieval. 

• Practical experience with RAG, information extraction, and structured validation (JSON Schema, Pydantic, or equivalent). 

• Sound judgement when reasoning about applicability, exceptions, missing evidence, and ambiguity, including recognizing when a system should decline to answer or require human review. 

• Experience constructing evaluation datasets, and the ability to write clearly about modelling decisions, tradeoffs, and failure modes. 

• Undergraduate, Master’s degree or PhD in Computer Science / Machine Learning / Data Science / Artificial Intelligence, or related disciplines. 

• Comfort operating at the boundary between a platform team and the domain engineering teams that consume it, balancing shared needs and central objectives against individual team priorities and timelines. 

• Excellent written and verbal communication, with the ability to work fluently with both technical and business stakeholders alike. 

Preferred experience 

• Semantic and symbolic tooling (RDF/OWL, SHACL, Datalog, DMN, production rule engines, policy-as-code).  

• Agentic systems with tools calling and validation-before-action.  

• Multi-tenant SaaS with strict isolation and audit requirements.  

• Experience in a high-accountability domain such as pharmaceutical or food labelling, and legal technology. 

• Experience in packaging graphics or the wider graphic arts industry