Job description

ScaleOps is redefining autonomous cloud and AI infrastructure. We're on a mission to free DevOps and platform engineers from manual resource management so they can focus on innovation, not tuning resources. The results: maximized performance and a reduction of cloud costs by up to 80%. As the category leader in Autonomous Cloud and AI Infrastructure Resource Management, we're trusted by leading enterprises including Adobe, Wiz, Epic Games, Northwestern Mutual, Coinbase, DocuSign, and Fortune 100 companies to autonomously manage their most critical production environments. Backed by Insight Partners, Lightspeed Venture Partners, and other leading VCs with over $210M in funding, ScaleOps is the leading player in a massive and growing market. We are building the autonomous infrastructure management platform that will power the next decade of enterprise compute. What You'll Be Doing This is an AI engineer role inside the company. Your “customer” is the GTM organization (Sales, SEs, Customer Success, Marketing), and your job is to embed with them, find the workflows that eat their time, and ship AI agents and automations that actually get used. You’ll work US hours alongside the field team and be the bridge between GTM and the AI platform team. Sit with SEs, TAMs, AMs and AEs to map their real workflows: account research, call prep, deal reviews, renewals, follow-ups, reporting. Build and ship agents and automations on top of our internal platform and connectors (Salesforce, Gong, Slack, Google Workspace and more). Turn rough requests into scoped solutions, iterate with users weekly and measure adoption and time saved. Write and maintain skills, prompts and evals so quality stays high as models and data change. Feed what you learn back to the platform team: missing integrations, reusable components, common failure modes. Train GTM users to get more out of AI tools and spread working patterns across the team. What You'll Bring 2+ years as a software or AI engineer, ideally with customer-facing, solutions or FDE experience. Deep hands-on experience with Claude and ChatGPT: you know how to get real results out of them and how to turn that into process automation. Comfortable with APIs and integrations; experience with CRM and sales tooling. Excellent communication: you can sit with a sales rep, understand the pain and explain trade-offs clearly. Product mindset: you care whether something gets adopted, not only whether it runs. Native Hebrew speaker. Nice to have Python hands-on experience building LLM-based applications and agents (tool use, MCP, RAG, evals). Experience at a B2B SaaS or infrastructure/DevOps company.

Requirements

Must-haves

What You'll Be Doing
Sit with SEs, TAMs, AMs and AEs to map their real workflows: account research, call prep, deal reviews, renewals, follow-ups, reporting.
Build and ship agents and automations on top of our internal platform and connectors (Salesforce, Gong, Slack, Google Workspace and more).
Turn rough requests into scoped solutions, iterate with users weekly and measure adoption and time saved.
Write and maintain skills, prompts and evals so quality stays high as models and data change.
Feed what you learn back to the platform team: missing integrations, reusable components, common failure modes.
Train GTM users to get more out of AI tools and spread working patterns across the team.
2+ years as a software or AI engineer, ideally with customer-facing, solutions or FDE experience.
Deep hands-on experience with Claude and ChatGPT: you know how to get real results out of them and how to turn that into process automation.
Comfortable with APIs and integrations; experience with CRM and sales tooling.
Excellent communication: you can sit with a sales rep, understand the pain and explain trade-offs clearly.
Product mindset: you care whether something gets adopted, not only whether it runs.
Native Hebrew speaker.

Nice-to-haves

Python hands-on experience building LLM-based applications and agents (tool use, MCP, RAG, evals).
Experience at a B2B SaaS or infrastructure/DevOps company.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aipythonllm

Platform: greenhouse
Effort: quick