StackAI→
Forward Deployed Engineer - Healthcare at StackAI in New York City, NY
Skills
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 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.