Applied AI Engineer

Action1 is an autonomous endpoint management platform trusted by many Fortune 500 companies. Cloud-native, infinitely scalable, highly secure, and configurable in minutes—it just works and is always free for the first 200 endpoints with no functional limits.

By pioneering autonomous OS and third-party patching with peer-to-peer patch distribution and real-time vulnerability assessment (without requiring a VPN), Action1 eliminates routine IT labor, helps prevent ransomware and security risks, and protects the digital employee experience.

In 2025, Action1 was recognized by Inc. 5000 as the fastest-growing private software company in America.

The company is founder-led by Alex Vovk and Mike Walters, entrepreneurs who previously founded Netwrix, a multi-billion-dollar cybersecurity company.

We are looking for a Applied AI Engineer to build and operate LLM-powered internal solutions, tools, automations, and customer-facing AI features end-to-end. 

We are not building AI for the sake of AI, or to follow hype or impress investors. We care about AI that works in real life and solves actual problems. 

Technology choices and product decisions are not simply handed down from the top. You will have room to shape the solution, challenge assumptions, choose the right tools, and influence the result. 

• →Build and operate LLM-powered internal solutions, tools, and automations end-to-end  

• →Ship customer-facing AI features  

• →Drive technical decisions from design through production rollout  

• →Own systems in production, including monitoring, reliability improvements, and incident response participation  

• →Build and maintain LLM evaluation workflows, including quality/safety checks, baselines, and regression detection  

• →Partner with R&D, product, and security teams  

• →Evaluate emerging AI technologies and patterns, and advocate adoption when justified  

• Strong software engineering fundamentals with hands-on ownership of production systems; Python required  

• Solid LLM application development experience, including context design, structured outputs, and orchestration  

• Hands-on experience with LLM evaluations, including experiment design, regression detection, gating, and observability  

• Experience building and operating backend services/APIs in production  

• Experience designing multi-service architectures and reliable integrations, including stable service contracts, retries, and failure recovery  

• Working knowledge of operating production databases, including schema design, indexing, migrations, and backup/restore  

• Hands-on experience with containerized workloads  

• Ability to balance output quality, latency, and cost in production AI systems 

• Experience with agentic systems or RAG, including evaluation 

• Experience building lightweight user interfaces 

• Experience shipping high-adoption internal developer or operations tooling  

• JavaScript/TypeScript  

• AWS deployment and operations experience 

• Experience with runtime isolation and sandboxing for sensitive execution  

• Experience in B2B SaaS, security, compliance, or data governance contexts 

You do not need to have experience with every tool listed below, but this is the environment we work with now:

• Python, uv, FastAPI, PydanticAI
• PostgreSQL, Docker, Docker Compose
• AWS: EC2, ECR, S3, Lambda
• Interfaces and tooling: Streamlit or similar tools, pytest, ruff, pyright, OpenTelemetry