Top Tech Jobs & Startup Jobs in Charlotte, NC

Build and operate backend services and production applications incorporating LLMs, machine learning, retrieval, tool use, and agentic workflows. Partner with data scientists to productionize models, develop AI evaluation and observability systems, support clinical and operational data workflows, and improve scalable cloud-native architecture. The role also involves secure data handling, reliability engineering, cross-functional collaboration, and production support for healthcare AI products.

Leads end-to-end healthcare integrations between the company’s platform and hospital EHRs, including architecture, interface development, data mapping, validation, testing, go-live, monitoring, and production support. Advises senior health system stakeholders, troubleshoots HL7 and FHIR interoperability issues, improves integration automation, manages incidents, and influences platform strategy. Requires deep HL7v2, interface engine, database extraction, FHIR, SQL, security, and healthcare interoperability expertise, plus strong client communication.

Own and evolve the AWS and Kubernetes platform, build Terraform modules, Helm charts, and GitHub Actions workflows, enhance platform capabilities with Go/Python, implement monitoring and runbooks, ensure secure CI/CD and data segregation, and support Node.js/React/Next.js engineering teams.

Design, build, and launch backend services to process and analyze clinical data at scale. Collaborate cross-functionally, enforce secure coding and privacy, support production systems, and drive technical improvements.

Lead a portfolio of enterprise health system clients to drive implementation, adoption, and value realization of AI-powered clinical documentation. Build executive and clinical relationships, run governance and business reviews, coordinate cross-functional teams, monitor adoption and outcomes, advocate client needs to Product, and support renewals and account growth.

The Staff Machine Learning Engineer will develop and maintain MLOps tools, optimize model performance, and collaborate on ML system architecture with a focus on enhancing developer experience.