Staff Software Engineer – AI Engineering

About the role

Tebra’s Revenue Cycle Management team is looking for a Staff Software Engineer to lead the design, development, and adoption of AI‑native capabilities across its billing and revenue‑cycle platform. This senior individual contributor role blends deep software engineering expertise with hands‑on experience building production AI systems to transform how healthcare organizations manage payments, claims, and operational workflows.

Key responsibilities

• Identify and implement practical AI opportunities in backend services and business workflows to improve efficiency, accuracy, or decision support.
• Design, build, and deploy production‑ready AI‑enabled services that combine application logic, APIs, and AI models for real customer use cases.
• Integrate large language models, machine‑learning models, and external AI services into existing systems using reliable, observable, and maintainable engineering patterns.
• Develop bounded, auditable AI workflows with clear fallback behavior, evaluation, and human‑in‑the‑loop review where needed.
• Partner with product, design, data, and operations teams to turn workflow pain points into scalable software solutions with measurable impact.
• Lead software development using Spring Boot, Java, React, and cloud technologies; conduct code reviews and mentor junior engineers.
• Architect cloud‑native applications on AWS, Azure, or Google Cloud, ensuring scalability, reliability, and cost‑efficiency.

Required profile

• Senior‑level software engineer with a proven track record of delivering production‑grade AI systems.
• Strong ability to translate complex business challenges into intelligent, measurable software solutions.
• Excellent collaboration skills to work across product, design, data, and operations teams.

Required skills

• Spring Boot
• Java
• React
• AWS, Azure, Google Cloud
• Large Language Models (LLMs) and Machine Learning model integration
• API design and integration
• Cloud‑native architecture and reliability/observability patterns