Forward Deployed AI Engineer
Mid-level Solutions & Sales Engineering role at StackAI. San Francisco · full-time · $153,000–$200,000 base.
Growth Roles summary, based on the employer's posting.
What you'll do
- You'll tune and support StackAI solutions for strategic enterprise customers
- You'll uncover requirements and stakeholder connections across target customer organizations
- You'll turn customer input into Python backend and React/Next.js TypeScript frontend changes
- You'll shape opportunities through proposals, stakeholder pitches, demos, forecasting, and closing
- You'll represent StackAI at enterprise events and inform go-to-market direction
What you bring
- Bring at least three years in data science, software development, or generative AI
- Know AI/ML, RAG pipelines, LLM workflows, and enterprise data analytics
- Have experience working with strategic enterprise accounts, ideally Fortune 500 organizations
- Be ready to help build a business in a fast-moving setting
- Be able to travel between ten and twenty percent of the time
Who this fits
This suits an experienced engineer who can combine enterprise customer work, technical solution design, and direct product development. The role is hybrid in San Francisco, focuses on strategic enterprise accounts, and includes regular travel. You should be comfortable with both customer-facing sales activity and contributions to the codebase.
From the employer
About the Company
StackAI (by Asana) is a no-code AI workflow platform that empowers companies to build, deploy, and scale AI-powered workflows easily. With thousands of users and rapidly growing enterprise adoption, our mission is to democratize access to LLMs and bring AI into the hands of every business operator, not just developers. An Asana Company, we're building the foundational platform for the AI-driven future of work
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.
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