Updated 18h ago

Applied AI Engineer - Operations at Action1 · AI Job Signal

Action1 is an autonomous patch management platform trusted by many Fortune 500 companies. Cloud-native, infinitely scalable, highly secure, and configurable in 5 minutes, it just works and is always free for the first 200 endpoints, with no functional limits. By pioneering autonomous OS and third-party application patching, Action1 helps organizations eliminate routine work, reduce ransomware and security risks, and protect the digital employee experience. It provides real-time vulnerability assessment and remediation across remote and on-site endpoints without requiring a VPN, while peer-to-peer patch distribution reduces bandwidth use on local networks.

In 2026, Action1 was recognized as the fastest-growing founder-led cybersecurity company in America on the Inc. 5000. Action1 is led by industry veterans Alex Vovk and Mike Walters, American entrepreneurs who founded Netwrix, which has grown into an industry-leading, multibillion-dollar cybersecurity company.

The Role

We are looking for a senior Applied AI Engineer to join Operations and improve how our business teams work. You will work directly with Support, Finance, Sales Ops, HR, IT, and Legal to turn repetitive tasks and disconnected workflows into reliable AI-powered tools and automations.

We build AI to solve real problems, not to follow hype or impress investors. You will investigate how work gets done, propose improvements, and own technical decisions and delivery. We expect you to take initiative, deliver useful results quickly, and improve solutions through short iterations and feedback.

What you’ll do

Work with teams to understand their workflows, find bottlenecks, and propose improvements. Agree process changes with the COO and relevant teams before implementation.
Build AI tools and automations that connect business systems and handle tasks such as document processing or ticket triage. Deliver working versions quickly and improve them through feedback.
Prioritize opportunities by time saved, error reduction, and risk. Measure whether solutions deliver those benefits, reduce costs, and are actually used.
Run and support what you build. Test AI outputs, keep integrations reliable as systems change, and manage failures, latency, and cost.
Protect customer and employee data through limited access, minimal exposure, and redaction before model calls. Work with IT and Security on safeguards, audit trails, and human review where needed.
Evaluate AI tools and vendors, and recommend when to use an existing solution versus building one.
Document workflows and runbooks, train teams to use the solutions, and help them identify and request further automations.

What we’re looking for

4+ years in operations engineering, business systems, automation, or a similar role, with 1+ years building production LLM or AI-enabled workflows.
Hands-on experience with workflow automation platforms and REST APIs, webhooks, JSON, and OAuth.
Strong working knowledge of prompt design, retrieval-augmented generation, and agent/tool-use patterns.
Proficiency in Python or JavaScript, and comfort with cloud services; AWS preferred.
Experience with at least one major business system, such as NetSuite, Salesforce or ServiceNow.
Ability to translate business needs into technical solutions and explain trade-offs to non-technical leaders.
Sound judgment and practical knowledge of AI security, privacy, and data governance, including least-privilege access, sensitive-data protection, data minimization, and approval controls for sensitive actions.

Nice to have

Experience with Microsoft 365 / Teams integrations and Microsoft Graph.
Background in finance, revenue, or support operations processes.
Familiarity with process mining, BI tools, or SQL.
Experience with SOC 2, ISO 27001, or similar compliance frameworks.
Relevant AWS, Azure, or security certifications.
Familiarity with IT management, RMM, or endpoint security concepts.
Experience shipping software with coding agents such as Claude Code or Codex, including harnesses, development loops, reusable skills, and context and specification management.
Hands-on ownership of production backend services/APIs, with practical database and container experience.
Systematic AI evaluation using datasets, metrics, baselines, reproducible experiments, and regression detection.
Deeper ML foundations, statistical experimentation, and model training or fine-tuning.
Experience with runtime isolation and sandboxing for sensitive execution.

What Success Looks Like

First 30 days: Understand key workflows and systems, and deliver a prioritized automation backlog
First 90 days: Ship 3-5 production automations or integrations with measured impact, and publish AI usage and data-handling standards
First year: Establish a repeatable intake-to-deployment process, show clear gains in hours saved and error reduction, and make AI tooling a trusted part of daily operations

How We Measure Impact

Hours saved, cycle-time reduction, error and rework rates, automation reliability (uptime and failure rate), AI cost per task, and stakeholder satisfaction.