# Action1 — Applied AI Engineer

| Field | Value |
|---|---|
| **Date found** | 2026-06-04 |
| **Company** | Action1 |
| **Role** | Applied AI Engineer |
| **Location** | EU Remote |
| **Salary** | Undisclosed |
| **Job URL** | [LinkedIn](https://www.linkedin.com/jobs/view/4419013917/) |
| **Status** | New |

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## Company Research

| Field | Value |
|---|---|
| **Headquarters** | Houston, TX, USA |
| **Founded** | 2018 |
| **Employees** | ~124 |
| **LinkedIn** | [Action1](https://www.linkedin.com/company/action1corporation/) |
| **Website** | https://www.action1.com |
| **Blog** | — |

- **Product:** Cloud-native autonomous endpoint management platform for IT security and patch management
- **Customers:** SMB and enterprise IT teams needing remote patch management and endpoint security
- **Notable:** Inc. 5000 fastest-growing private software company 2025; 182% two-year revenue growth; bootstrapped/private

Indeed rating: No data (company profile not yet established on Indeed IE)

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## Job Summary

**What they do:** Action1 builds a cloud-native autonomous endpoint management platform that automates patch management and IT security for distributed workforces.

**The role:** Mid–Senior AI Engineer on the AI team, taking full ownership of internal LLM-powered tooling from design through production.

**Core work:**
- Build and operate LLM-powered internal solutions to automate engineering and business workflows
- Build and maintain LLM evaluation frameworks to measure output quality and reliability
- Drive technical decisions end-to-end (design → production rollout) for AI features

**Stack:** Python · FastAPI · PydanticAI · PostgreSQL · Docker · AWS (EC2, ECR, S3, Lambda) · LLM eval frameworks

**Work style:** EU Remote; fast-growing startup (~124 employees), IC-owned projects with direct impact

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## Score: 77%

| Dimension | Score | Justification |
|---|---|---|
| Agentic AI depth (25%) | 65% | Core work is LLM-powered internal automation and eval pipelines — applied LLM engineering, not multi-agent systems. Agentic work listed as nice-to-have, not core. |
| Tech fit (25%) | 65% | Strong Python/AWS overlap; PydanticAI is adjacent to LangGraph-style orchestration. No explicit LangGraph/LangChain, no open-source model fine-tuning. |
| Remote fit (25%) | 100% | EU Remote — no on-site requirement stated. |
| Company culture fit (15%) | 70% | Fast-growing AI-adjacent startup with strong revenue traction (Inc. 5000). Cybersecurity domain rather than pure AI-native; some enterprise process risk at scale. |
| IC/leadership balance (10%) | 90% | Explicitly IC: "drive technical decisions from design through production rollout" with no management responsibilities mentioned. |
| **Final (weighted)** | **77%** | |

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## Strengths

- Fully EU remote with high IC autonomy — design-to-production ownership matches Luca's preferred working style
- LLM evaluation frameworks and internal AI tooling are directly in scope of applied AI engineering experience
- Inc. 5000 growth trajectory signals engineering velocity and stability; stage is right for meaningful contribution

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## Weaknesses & Risks

- Agentic AI is a nice-to-have, not core — primary focus is LLM-powered internal automation, which may be less cutting-edge than pure agentic systems work
- Salary completely undisclosed — risk of being below €100k target for a ~124-person security startup
- Cybersecurity domain (endpoint management) is not a natural fit; domain knowledge gap could be a disadvantage in interviews

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## Suggestions

- Lead with LLM evaluation experience and production-grade AI systems deployment
- Emphasise end-to-end ownership of AI pipelines in previous roles; this team values engineers who drive from design to prod
- Ask about salary band early to validate against €100k+ target before investing interview time

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## Interview Tracker

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