About the company
We build AI infrastructure for enterprises — specifically, a platform that takes complex, heterogeneous data and auto-generates knowledge graphs from it. Those graphs power LLM agents that automate workflows previously requiring skilled human judgement. Our two core verticals are pharma and financial services. We sell outcomes: compliance teams getting ahead of regulatory change, advisors spending less time on manual research.
The role
This role bridges pre-sales, client deployment, and product engineering. You will work directly with enterprise clients — scoping and pitching solutions, deploying our platform on live data, building prototypes, and feeding what you learn back into the product. The product is early-stage and you will be working at codebase level. You need to be able to hold a room with a CTO and write Python on the same day.
What you'll do
• Lead technical pre-sales — discovery sessions, solution architecture, and pitching to senior client stakeholders.
• Configure and deploy the platform at client sites across pharma and financial services.
• Inspect auto-generated knowledge graphs for correctness and identify which workflows are worth automating.
• Build working prototypes on real client data — live, explainable, specific enough to hand off. • Enable implementation partners and manage the technical handoff to production.
• Work directly with the product and engineering team to translate field learnings into new functionality.
What we're looking for
• Experience in pharma, financial services, or both — you know what's worth automating and why.
• Comfortable working directly with a Python codebase — building, debugging, and extending features.
• Graph literacy — Neo4j, SPARQL, or equivalent. Can inspect a knowledge graph and judge its fitness.
• Agent fluency — has built LLM agents in production. Knows what goes wrong.
• Strong communicator — can translate complex technical concepts to non-technical senior stakeholders.
• Pre-sales experience — comfortable leading discovery, scoping engagements, and presenting solutions.
• Integration thinking — can map a client's data environment to the connectors needed.
Nice to have
• GraphRAG, multi-hop reasoning, or graph quality assessment experience. • Prior FDE or technical consulting background.
• Regulatory data fluency — MiFID II, MLR, ACPR.
• Experience enabling SI or implementation partners.
• Streamlit or similar rapid prototyping tools.
Why this role
Most enterprise AI deployments stall between the model and the workflow. This role exists to close that gap across pharma and financial services — with a product built specifically for it. You will have direct input into how the product evolves, work on real client problems, and own outcomes you can point to.
Artificial Intelligence Engineer • City of London, London, United Kingdom
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