Presales Solution Architect - Knowledge Graph and Semantic AI

Job Description

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.

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About Us

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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.

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Datavid, a C5i company, is the group's knowledge graph and semantic capability. 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.

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What You Will Do

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Lead the client conversation. You will present to CIOs, CDOs, heads of R&D, enterprise architects and business sponsors, often in the same meeting, covering both the architecture and the business case. You will run discovery workshops, work through conflicting requirements between client teams, and answer detailed technical challenge from client architects.

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Qualify and shape opportunities. 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.

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Design the solution. 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.

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Demonstrate and prove. 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.

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Write the technical response. You will author the solution, architecture, security and delivery-approach content of RFI, RFP and proposal responses, working to the group's evidence discipline.

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Bridge to delivery. You will hand won work to the delivery organisation with an architecture, an assumptions register and a risk log that the delivery lead recognizes.

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Work as one team. You will work day to day with sales, delivery architects, engineering and bid colleagues across the US, Europe and India. Much of that work is shared, asynchronous and written, and the architecture you produce is reviewed by the people who will build it.

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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. Where the group's partner relationships matter to an opportunity, you will support joint technical conversations with those partners.

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Essential Experience

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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. The remaining two are here because the role is unviable without them. Candidates should be ready to give specific examples against each.

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• Technical depth, earned in build. 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.
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• Knowledge graphs and semantics in practice. 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.
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• Client-facing presales or consulting experience. 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 have led discovery sessions and workshops with enterprise clients, presented architecture to stakeholders who were not all on your side, and worked alongside a sales lead on qualification, scoping and pricing input. This is not a quota-carrying position, but you understand how a deal is put together and what makes one worth pursuing.
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• Communication and interpersonal range. You can pitch the same material to an executive sponsor, a business owner and an enterprise architect. You work across functions and geographies in a matrixed organisation, and you can disagree with a client or a colleague and keep the relationship.
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• Written technical persuasion. You can write a solution narrative an enterprise architect will respect and an executive sponsor will finish (proposals, RFP responses, architecture notes).
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Desirable Experience

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None of the following is a barrier to applying and no candidate is expected to hold all of them.

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• Retrieval-augmented generation and agentic architectures: orchestration, planner and tool patterns, grounding, evaluation and guardrails.
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• Hands-on familiarity with graph and search platforms such as Neo4j, Amazon Neptune, Stardog, GraphDB, MarkLogic, Elasticsearch or OpenSearch and with vector stores.
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• Lakehouse and cloud data platforms: Databricks, Snowflake, AWS, Azure or GCP and how a semantic layer is positioned alongside them.
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• Sector depth in life sciences and pharma, scientific publishing, medtech, financial services or manufacturing, particularly where regulatory traceability is a requirement.
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• Master data management, data governance frameworks or FAIR data implementation.
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• Experience presenting at orals or finalist sessions, and representing a firm at industry events, webinars or partner sessions.
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• Working in a global delivery model with offshore engineering teams.
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• Prior work in a services or consultancy environment where solutions were sold and then delivered by the same organisation.
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Selection Process

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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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C5i is proud to be an equal opportunity employer. We are committed to equal employment opportunity regardless of race, color, religion, sex, sexual orientation, age, marital status, disability, gender identity, etc. If you have a disability or special need that requires accommodation, please keep us informed about the same at the hiring stages for us to factor necessary accommodations.