ashby
AI & Support Automation - Engineer
CertifyOS
Remote, IND
Full-time
Mid · 3+ years experience
Remote
Discovered 1 weeks ago
PythonJavaScriptTypeScriptLLMRAGMCP
Free
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Role Overview
We are looking for hands-on engineers who can lead support automation initiatives while personally designing and building AI solutions end-to-end. This is a role for someone who can identify support problems, build AI/automation solutions, deploy them to production, and continuously improve them.
Key Skills for This Role
PythonJavaScriptTypeScriptLLMRAGMCP
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About CertifyOS
CertifyOS is building the data infrastructure that powers modern healthcare.
Today, healthcare organizations rely on fragmented and outdated provider data. This creates unnecessary administrative work, regulatory risk, and higher costs across the system. We’re solving that problem.
Our API-first platform automates provider licensing, enrollment, credentialing, and network monitoring by connecting directly to hundreds of primary data sources. We help healthcare organizations maintain accurate, compliant, and reliable provider networks at scale.
Our vision is simple: One API. One provider ID. Frictionless provider data.
We’re backed by leading investors and built by a team with deep experience in provider data systems. At CertifyOS, we value authenticity, accountability, collaboration, results, and openness to feedback. We’re building a high-ownership team focused on solving real infrastructure problems that impact millions of patients.
Role Overview
We are looking for hands-on engineers who can lead support automation initiatives while personally designing and building AI solutions end-to-end. This is a role for someone who can identify support problems, build AI/automation solutions, deploy them to production, and continuously improve them.
Key Responsibilities
- Identify high-impact support processes for AI/automation. • Design, build, integrate, test and deploy AI-powered workflows and agents. • Develop solutions using LLMs, RAG, AI agents and workflow automation. • Build end-to-end AI pipelines covering knowledge/data ingestion, orchestration, model execution, tools/actions, evaluation and production monitoring. • Integrate AI with CRM, ticketing, knowledge bases and internal tools. • Monitor AI quality, accuracy, cost, latency and business impact; continuously improve solutions. • Partner with Product and Engineering to drive AI-led transformation.
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