Stack AI
The Enterprise AI Agents Platform
Enterprise AI Deployment Engineer
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.
- 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, or large analytic programs.
- Eagerness to build a business in a fast-paced environment.
- Ability to travel 10-20% of the time.
About Stack AI
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.
