Knowledge Engineer | Knowledge Graphs | Ontologies | Semantic | RDF | OWL | Python | Hybrid, London Full Time NEW

Knowledge Engineer | Knowledge Graphs | Ontologies | Semantic | RDF | OWL | Python | Hybrid, London

About the Role

Our mission is to improve the delivery and efficiency of healthcare.

We are building a platform to model and manage the flow of information within healthcare organisations, improving outcomes for patients, payers, and providers. We believe data handling in current healthcare systems is often complex and disconnected, leading to isolated and inefficient decision-making. To demonstrate how this technology can advance healthcare delivery and improve lives, we build and deploy products for healthcare organisations in multiple international markets.

We are an energetic health-tech startup of around forty employees. Our team is growing as we explore new markets and opportunities. We are passionate about applying technology to meaningful challenges. New joiners will have a significant impact on the direction of the company and its culture.

Our Products

AI Platform

Our products are built on a Semantic AI platform that enables healthcare organisations to access advanced AI capabilities for their own use cases and applications. Partners may use platform APIs directly or collaborate with us to develop tailored applications.

Operational AI Products

We develop AI-powered tools that support healthcare operations, including solutions that reduce administrative burden in processing clinical correspondence. These systems help reduce staff time spent identifying coding requirements, suggest follow-up tasks, and optimise workflows. The goal is to save time and cost, improve audit performance, and build staffing resilience.

The Role

We are seeking a Knowledge Engineer to design, evolve, and operationalise the structured clinical knowledge that underpins our AI systems.

This is a mid-to-senior individual contributor role focused on classical knowledge engineering and graph data curation. The role combines deep semantic expertise with real-world accountability: ensuring that clinical and administrative concepts are modelled correctly, knowledge graphs remain reusable across customers, and ontologies scale without becoming brittle or overly bespoke.

You will play a critical role in ensuring clinical intelligence is durable, reusable, and transparent — embedding knowledge in structured systems rather than in individual expertise or ad-hoc code.

This role includes collaboration with customers and internal teams and is offered on a hybrid basis from our London office.

Core Responsibilities

Ontology & Knowledge Graph Design

• Lead the design, extension, and maintenance of medical ontologies, terminologies, and knowledge graphs.
• Ensure semantic consistency and concept reuse across customers and products.
• Model clinical and administrative concepts to support machine reasoning, not just human categorisation.
• Ensure interoperability with relevant standards (e.g., SNOMED CT) and alignment with real-world workflows such as quality measures.

Customer-Facing Knowledge Elicitation

• Engage directly with customers to understand source documents, coding practices, and workflow nuances.
• Translate customer feedback and AI failure modes into graph corrections, schema updates, or ontology extensions.
• Identify when system errors reflect knowledge modelling gaps rather than model performance issues.
• Act as a credible interface between operational realities and system structure.

Knowledge Governance & Schema Authority

• Own the representation, versioning, and evolution of clinical concepts within knowledge systems.
• Define provenance and lineage requirements for clinical data.
• Coordinate major ontology changes with Product and AI leadership while retaining execution ownership.
• Ensure knowledge artefacts are well-documented, transparent, and reusable across teams.

Automation & Tooling

• Collaborate to design and build pipelines that ingest structured, semi-structured, or unstructured data into graph systems.
• Work with engineers to automate ontology updates and reduce manual curation overhead.
• Improve tooling to prevent knowledge bottlenecks.
• Ensure robust documentation and knowledge transfer to support organisational adoption.

Requirements

Minimum requirement: A bachelor’s degree in computer science with a focus on logical systems (or equivalent education), plus at least two years of commercial experience delivering knowledge-based systems in production environments.

Semantic & Ontology Expertise

Strong experience in ontology engineering and semantic technologies, including:

• OWL
• RDF
• SHACL

Additional expectations:

• Deep understanding of logical systems and knowledge modelling
• Experience with constraint validation and inference
• Ability to design ontologies that are expressive yet operationally practical

Graph Infrastructure & Querying

• Hands-on experience with triple stores such as Stardog, GraphDB, or Apache Jena/Fuseki
• Experience with graph databases (e.g., AWS Neptune) and hybrid storage models
• Advanced SPARQL skills
• Comfortable with SQL and NoSQL where appropriate
• Experience designing performant graph queries for production systems

Automation & Data Engineering

• Experience building ingestion pipelines to populate graphs at scale
• Ability to collaborate effectively with software engineers to operationalise semantic infrastructure
• Strong appreciation for documentation, versioning, and knowledge durability

Domain & Operating Context

• Experience working with healthcare data, clinical terminologies, or administrative coding systems is strongly preferred
• Ability to engage confidently with customers and translate domain knowledge into structured representations
• Comfortable working in a startup environment with high ownership and initiative

Personal Attributes

• Systems thinker with strong attention to conceptual clarity
• Pragmatic rather than academically purist
• Comfortable balancing theoretical correctness with real-world constraints
• Motivated by building durable, reusable knowledge systems that improve AI reliability

Hiring Process

• Introductory screening interview (30 minutes)
• Technical and domain interview with Applied AI and Product leadership
• Final interview and offer

Benefits

• Competitive salary
• Company pension
• 25 days of paid annual leave (pro-rata)
• Flexible hybrid working environment
• Employee Assistance Programme
• Modern office in central London with complimentary refreshments

Knowledge Engineer | Knowledge Graphs | Ontologies | Semantic | RDF | OWL | Python | Hybrid, London