Key Facts

Remote From:

Israel, New York (USA), United States

Full time

Mid-level (2-5 years)

English

Hard Skills

Other Skills

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Public Speaking
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Presentations
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Entrepreneurship
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Teamwork
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Communication

Roles & Responsibilities

3+ years of experience in data science, software development, or generative AI
Experience with strategic enterprise accounts, preferably Fortune 500
Expertise in AI/ML, RAG pipelines, LLM workflows, and enterprise data analytics
Ability to travel 10-20% of the time

Requirements:

Optimize and support solutions within strategic accounts on the Stack AI platform
Map requirements and relationships within target enterprise customer offices
Contribute directly to the StackAI codebase, translating customer feedback into platform improvements across the Python backend and React/Next.js TypeScript frontend
Forecast and close high-value opportunities

StackAI

About StackAI

Enterprise AI trusted by IT - power your GenAI strategy with Stack AI. Backed by YC and Google. We are a small dedicated team of insanely talented individuals relentlessly pushing the boundaries of what’s possible with AI. We are pioneering a new horizontal platform allowing anyone to build and deploy AI agents and automations. Companies across industries—including healthcare, legal, financial, logistics, and defense—use StackAI make their organizations faster, more efficient, and scalable, leveraging the power of AI. With strong backing from YC and Google, we're set to double our team size in 2025. This is an exciting time to join if you want to build a category-defining product with strong customer momentum. We're looking for user-centric, craft-focused, creative minds who work hard but don't take themselves too seriously. We're ambitious yet pragmatic. We move fast, but care about the important details. We're hiring for a range of roles. Reach out if you'd like to learn more!

Founded: 2018

Company size: 51 - 200

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Job description

About the role

We’re seeking an experienced engineer to deploy enterprise-grade AI solutions, focusing on Retrieval-Augmented Generation (RAG) pipelines and large language model (LLM) workflows. This role is vital to expanding our reach with Fortune 500 and enterprise clients across various industries.

Role Overview:

You will integrate large language models into enterprise operations, working with strategic accounts to align solutions and technical approaches. Using the Stack AI platform, you'll also partner with clients to co-design solutions for emerging needs.

Responsibilities:

Optimize and support solutions within strategic accounts on the Stack AI platform.
Map requirements and relationships within target enterprise customer offices.
Pursue opportunities and provide feedback on our go-to-market strategy.
Contribute directly to the StackAI codebase, translating customer feedback into platform improvements across the Python backend and React/Next.js TypeScript frontend.
Forecast and close high-value opportunities.
Write proposals, pitch stakeholders, and lead product demos.
Evangelize Stack AI at enterprise events.

Requirements:

3+ years of experience in data science, software development, or generative AI.
Experience with strategic enterprise accounts, preferably Fortune 500.
Expertise in AI/ML, RAG pipelines, LLM workflows, and enterprise data analytics.
Eagerness to build a business in a fast-paced environment.
Ability to travel 10-20% of the time.

About StackAI

Stack AI is a no-code drag-and-drop tool to quickly design, test, and deploy AI workflows that leverage Large Language Models (LLMs), such as ChatGPT, to automate any business process.

Our core value is to make it extremely easy to build arbitrarily complex AI pipelines using a visual interface that allows you to connect different data sources with different AI models.

Our customers use Stack AI to build applications such as:

Chatbots and Assistants: AI agents that interact with users, answer questions, and complete tasks, using your internal data and APIs.
Document Processing: apps to answer questions, summarize, and extract insights from any document, no matter how long.
Answer Questions on Databases: connect GPT-like models to databases (such as Notion, Airtable, or Postgres) and ask questions about them.
Content Creation: generate tags, summaries, and transfer styles or formats between documents and data sources.