# GraphAware — AI Engineer

| Field | Value |
|---|---|
| **Date found** | 2026-05-21 |
| **Company** | GraphAware |
| **Role** | AI Engineer |
| **Location** | EU + UK (Fully Remote) |
| **Salary** | Undisclosed + equity |
| **Job URL** | [LinkedIn](https://www.linkedin.com/jobs/view/4414615551/) |
| **Status** | New |

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## Company Research

| Field | Value |
|---|---|
| **Headquarters** | London, United Kingdom |
| **Founded** | 2013 |
| **Employees** | 51–200 (47 on LinkedIn) |
| **LinkedIn** | [company/graphaware](https://www.linkedin.com/company/graphaware/) — 12K followers |
| **Website** | [graphaware.com](https://graphaware.com) |
| **Blog** | [graphaware.com/blog](https://graphaware.com/blog/) |

- **Product:** Graph-powered intelligence analysis platform (Hume) for connected data analytics in mission-critical environments
- **Customers:** Government and law enforcement agencies globally
- **Notable:** Small team (~47 on LinkedIn); offices in UK, Czech Republic, Italy, and Australia; specialises in ML on interconnected graph data

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## Job Summary

**What they do:** Graph analytics and intelligence analysis software company. Hume platform powers connected data analytics for government and law enforcement agencies globally.

**The role:** First dedicated AI Engineer — full ownership of Hume Maestro, the core AI component of the platform.

**Core work:**
- Architect agentic AI workflows: tool use, reflection, multi-step reasoning, multi-agent orchestration
- Integrate GenAI and LLM capabilities into the Hume platform end-to-end
- Define AI best practices and mentor other engineers on responsible LLM integration

**Stack:** Python or Java · LangChain · LangGraph · HuggingFace · vector stores · Docker · CI/CD

**Work style:** Fully remote, EU + UK

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## Score: 81%

| Dimension | Score | Justification |
|---|---|---|
| Agentic AI depth (25%) | 80% | Genuinely agentic: tool use, reflection, multi-step reasoning, multi-agent orchestration with LangChain/LangGraph at the core of the product |
| Tech fit (25%) | 75% | Strong overlap on LangChain, LangGraph, Python, HuggingFace, RAG, vector stores; missing CrewAI, PyTorch, Claude/Anthropic APIs specifically |
| Remote fit (25%) | 100% | Explicitly fully remote across EU + UK |
| Company culture fit (15%) | 62% | Technical culture with autonomy values; graph analytics company extending into AI (not AI-native); government/law enforcement clients may introduce slower cycles |
| IC/leadership balance (10%) | 80% | Pure IC role as first AI engineer; informal enablement of colleagues but no management |
| **Final (weighted)** | **81%** | Strong remote + agentic depth offset by non-AI-native company |

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## Strengths

- First AI Engineer hire = full architectural ownership of Hume Maestro from day one
- Directly matches Luca's core stack: LangChain, LangGraph, agentic workflows, RAG, vector stores
- Fully remote EU/UK — ideal location fit
- Real production product with paying government customers — not a POC or research role
- Equity participation adds upside to the undisclosed base

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## Weaknesses & Risks

- Salary completely undisclosed — must verify early (target floor is €110k)
- Not an AI-native company; primary product is graph analytics and the AI layer is being bolted on
- Government/law enforcement clients can mean on-premises deployments, compliance constraints, and slower iteration cycles
- Java expected alongside Python — Luca's stack is Python-first; Java familiarity needed
- Competitive: 100+ applicants already

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## Suggestions

- Lead with LangGraph/LangChain production experience from Fenergo (agentic document parser) and cybersecurity role (text analysis platform)
- Emphasise owning the full arc from POC to production — exactly what "first AI engineer" needs
- Ask early about base salary range and equity structure before investing time in interviews
- Probe the government client model: how much does compliance/on-prem affect build speed?
- Highlight mentoring/enablement experience (3-person team at cybersecurity role) — they explicitly want someone who raises the team's GenAI capability

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## Interview Tracker

| Stage | Date | Notes |
|---|---|---|
| Applied | | |
| Recruiter screen | | |
| Technical interview | | |
| Final round | | |
| Offer / Outcome | | |