Forward Deployed Engineer - Healthcare

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 strategic healthcare accounts, especially across provider, payer, and adjacent healthcare sectors.
• Experience with healthcare enterprise accounts, especially in payer/provider environments, including EHR integrations, claims workflows, and working with PHI securely.
• Life sciences or medical device experience is a plus.

Role Overview

• You will integrate large language models into healthcare enterprise operations, working with strategic accounts to align solutions and technical approaches.
• Using the StackAI platform, you'll partner with clients to co-design solutions for operational and clinical-adjacent workflows.
• We're looking for someone with experience in the payer/provider space, including EHR integrations, claims processing, and working with PHI securely.
• Experience in life sciences or medical devices is a strong plus, especially in workflows such as CAPA, complaints, quality, and regulatory operations.

Responsibilities

• Optimize and support solutions within strategic healthcare accounts on the StackAI platform
• Work directly with payer, provider, and healthcare customers to understand workflows, systems, and implementation requirements
• Design and deploy AI workflows for use cases such as claims processing, prior authorization, clinical documentation, patient operations, and knowledge retrieval
• Support integrations with EHRs, healthcare data systems, APIs, and enterprise tools
• Help customers build solutions that account for PHI, HIPAA, access controls, and auditability requirements
• Map

requirements

and relationships within target customer organizations

• Pursue opportunities and provide feedback on go-to-market strategy for healthcare accounts
• Contribute directly to the StackAI codebase, translating customer feedback into platform improvements across the Python backend and React/Next.js TypeScript frontend
• Write proposals, pitch stakeholders, and lead product demos
• Evangelize StackAI at enterprise events and customer meetings

Requirements

• 3+ years of experience in data science, software development, solutions engineering, or generative AI
• Experience working with strategic enterprise accounts, preferably in healthcare
• Experience in the payer/provider space, especially with EHR integrations, claims workflows, revenue cycle, or payer/provider operations
• Familiarity with handling PHI and sensitive healthcare data in production environments
• Experience with strategic enterprise accounts, preferably Fortune 500.
• Expertise in AI/ML, RAG pipelines, LLM workflows, and enterprise data analytics
• Strong communication skills and ability to work across technical and business stakeholders
• Eagerness to build in a fast-paced environment
• Ability to travel 10–20% of the time

Preferred Qualifications

• Experience with healthcare interoperability standards such as FHIR, HL7, X12, or EDI
• Experience with provider or payer platforms such as Epic, Cerner, athenahealth, or similar systems
• Background in life sciences or medical devices
• Familiarity with workflows such as CAPA, complaint handling, quality systems, or regulatory documentation

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.