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.