Presales Solution Architect - Knowledge Graph and Semantic AI

This is a technical presales role in an organisation that sells enterprise AI, data and analytics services at scale and differentiates on semantic and knowledge graph engineering. The person in this seat owns the technical side of the sales conversation in the US market: qualifying opportunities alongside the account team, shaping the solution architecture, running demonstrations and proofs of value on the Datavid accelerator platforms and writing the technical content of proposals and RFP responses. The role sits between the account team and delivery and is accountable both for the technical credibility of what is promised and for whether it can be built as described. C5i is a global AI and analytics firm working with enterprise clients across life sciences, technology, financial services, industrials and consumer sectors. The group delivers data engineering, data science, augmented insights, enterprise generative AI services and competitive and market intelligence. Datavid designs ontology-driven data products, enriches data lakes and warehouses with a governed semantic layer, extracts entities and relationships from unstructured content, applies FAIR data principles and builds agentic and retrieval-augmented applications over graphs that can be audited and explained.

You will work alongside US sales leadership from first contact, joining discovery calls to establish what the prospect's data estate actually looks like, where meaning is currently lost and where can we help. You will produce the target architecture that sits behind each proposal: ontology and semantic model scope, ingestion and enrichment pipeline design, choice of graph and vector platform against the client's existing stack, retrieval and agentic patterns, and the governance model covering versioning, lineage, access control and audit. You will own Datavid accelerator platform demonstrations for the US market, tailoring them to the prospect's domain rather than showing a generic dataset, and you will scope and help run proofs of value and discovery sprints. You will author the solution, architecture, security and delivery-approach content of RFI, RFP and proposal responses, working to the group's evidence discipline. You will hand won work to the delivery organisation with an architecture, an assumptions register and a risk log that the delivery lead recognizes. Build the field's capability. You will feed reusable assets back into the library (demonstration scenarios, reference architectures, sector-specific answers, discovery question sets) and brief account teams so that routine technical questions stop reaching you. The requirements the hiring panel will not trade away are a genuine engineering background, working experience of knowledge graphs and semantic technology, and real client-facing experience in a presales or consulting setting. You have worked as an engineer, data engineer, solution architect or equivalent, and you can still read and reason about a data pipeline, an API contract and a deployment topology. You have designed, built or implemented against ontologies and knowledge graphs on real projects (RDF, OWL, SHACL and SPARQL, or labelled property graphs and Cypher, or both). You understand taxonomy and ontology modelling, entity resolution, and what separates a governed semantic layer from a schema with a graph database behind it. Five or more years in a customer-facing presales, solution consulting or technical consulting role at a consultancy, systems integrator or technology services firm. You can pitch the same material to an executive sponsor, a business owner and an enterprise architect. You can write a solution narrative an enterprise architect will respect and an executive sponsor will finish (proposals, RFP responses, architecture notes).

Hands-on familiarity with graph and search platforms such as Neo4j, Amazon Neptune, Stardog, GraphDB, MarkLogic, Elasticsearch or OpenSearch and with vector stores. Lakehouse and cloud data platforms: Databricks, Snowflake, AWS, Azure or GCP and how a semantic layer is positioned alongside them. Sector depth in life sciences and pharma, scientific publishing, medtech, financial services or manufacturing, particularly where regulatory traceability is a requirement. Master data management, data governance frameworks or FAIR data implementation. Working in a global delivery model with offshore engineering teams. Shortlisted candidates will be asked to provide a writing sample or complete a short written exercise, to present a solution to a simulated client panel, and to talk us through two or three opportunities they worked on, including one that did not go their way.

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