Lead Software Engineer, Conversational Agent Platform

athenahealth•Austin, TX

5d•$136,000 - $232,000•Hybrid

About The Position

The Lead Full Stack Software Engineer will design and build scalable, cloud-native web applications that support conversational AI experiences across athenahealth workflows. This role blends front-end and back-end engineering with platform thinking, helping shape the technical foundation for new products and capabilities. Based in Austin, TX and offered as a hybrid role, this position will report to the Senior Engineering Manager. The Conversational Agent Platform team builds the platform and guardrails for safe, intelligent conversational AI across Patient Experience workflows and channels. Teams across the organization are building conversational agents; this team provides the foundation they build on — including the infrastructure that hosts those agents, the interfaces that configure them, and the evaluation tooling that helps determine whether they are performing effectively. This is a new team with a strategically important charter. Many of the solutions we will deliver are still being defined, so this role will help shape both the technical direction and the operating model for the platform. The team is responsible for creating reliable, extensible services and experiences that support rapid experimentation while maintaining security, observability, and system quality.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience.
8 to 15 years of professional software engineering experience, including full stack application development.
Strong experience building production applications with React and TypeScript.
Experience developing production-grade backend services in Java and Spring Boot, including WebFlux or other reactive programming patterns.
Experience designing and maintaining APIs and services in cloud-native environments.
Hands-on experience with Postgres, including working knowledge of pgvector or other vector-search approaches.
Experience using Redis for caching, performance optimization, or distributed application support.
Experience deploying and operating services on Kubernetes and managing releases with Helm.
Experience implementing observability practices using OpenTelemetry, logging, metrics, and distributed tracing.
Experience with AI-enabled development or application workflows, including tools such as LangChain4j or similar frameworks.
Ability to work effectively in a hybrid environment and collaborate across engineering, product, and design partners.

Responsibilities

Develop and maintain full stack software solutions using React, TypeScript, Java, and Spring Boot (WebFlux) to support conversational AI platform capabilities.
Design and implement scalable APIs, user interfaces, and backend services that enable internal teams to configure, manage, and evaluate conversational agents.
Build and optimize data access patterns using PostgreSQL (Postgres) with pgvector to support retrieval, similarity search, and other AI-related data workflows.
Integrate and operate distributed caching and state-management patterns using Redis to improve application performance and reliability.
Collaborate with product, design, and engineering partners to translate platform requirements into maintainable technical solutions.
Contribute to platform architecture decisions for service boundaries, API design, observability, deployment patterns, and operational resilience.
Work with containerized workloads and deployment pipelines using Kubernetes and Helm to support repeatable releases and scalable environments.
Implement telemetry, tracing, logging, and performance monitoring practices using OpenTelemetry to improve system visibility and support incident response.
Evaluate and integrate AI development workflows and tooling, including LangChain4j, to improve engineering productivity and support responsible platform capabilities.
Apply AI tools and techniques in day-to-day engineering work and help the team identify practical ways to use AI to improve delivery, code quality, and evaluation workflows.
Participate in technical design reviews and code reviews across team-owned services and user experiences.
Support incident triage, root-cause analysis, and follow-up improvements for production issues.
Help define engineering standards, reusable patterns, and documentation for the team’s platform.
Mentor other engineers through pairing, feedback, and knowledge sharing.
Help improve CI/CD practices and release processes.
Assist with proof-of-concept work to assess emerging technologies and architectural approaches.
Partner with cross-functional teams to align delivery with customer needs and platform strategy.