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ML Engineer — knowledge graphs | Quantexa | London | Ganloss

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ML Engineer — knowledge graphs

AI role · Open offer

ML Engineer — knowledge graphs

Quantexa

Full-time
Senior
Graph intelligence
Hybrid
8 Finsbury Circus, London, England EC2M 7AZ, United Kingdom · Hybrid
London, United Kingdom
£88k–£118k + equity

Employer

Quantexa

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Posted on Ganloss

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At a glance

How you'll work, where, and key listing dates.

Employment: Full-time
Workplace: Hybrid
Location: 8 Finsbury Circus, London, England EC2M 7AZ, United Kingdom · Hybrid
Compensation: £88k–£118k + equity
Experience level: Senior
Team: Graph intelligence
Benefits & perks: Equity, pension, hybrid.
How to apply: Graph ML or entity resolution project summary.
Company site: www.quantexa.com
Listed: Jul 7, 2026
Updated: Aug 3, 2026

About this role

What the team is looking for and how this position fits.

Quantexa is hiring a Senior ML Engineer — knowledge graphs on the Graph intelligence team (Hybrid · London, United Kingdom). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.

You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

Quantexa resolves entities and builds graph intelligence for AML and fraud teams.

ML Engineer — knowledge graphs: graph neural networks, NLP entity linking, and production scoring.

What you will do

Deliver production-grade graph ML and entity resolution with measurable quality, latency, and cost targets.
Build offline evals and online monitoring before scaling to full traffic.
Collaborate with product, design, legal, or domain experts on safe rollout.
Write clear RFCs, runbooks, and postmortems for cross-team learning.

What we look for

Multiple shipped ML/LLM features—not coursework-only portfolios.
Strong Python, SQL, and experimentation discipline.
Clear communication for remote or hybrid stakeholders.
Work authorization for the listed country.

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
SQL
Analytical SQL for features, cohorts, label quality, and experiment analysis at scale.

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

Neo4jTool
Regular use of Neo4j in the LLM & generative AI stack—configuration, debugging, and team conventions.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: Graph ML or entity resolution project summary.
Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, SQL, and Neo4j.
You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · London, United Kingdom)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
You read the full listing (comp: £88k–£118k + equity, benefits in At a glance) and your expectations align.

Apply

Candidate accounts only: add your headline and bio on My account, and optionally a CV link or PDF so recruiters see your full résumé. Then write a short pitch below—applications use your login email.

What the employer asked for

Graph ML or entity resolution project summary.

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Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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