You will work with product owners, infrastructure owners, and partner engineers to turn ambiguous, high-impact problems into clean solutions. You'll own products that ETS teams depend on every day. You will also help define how AI agents work in production alongside humans, in the same codebases our engineers ship to daily.

The role mixes product engineering with hands-on integration work. You may start closer to one than the other, and you should be comfortable moving between them as the work changes.

We are looking for strong full-stack engineers who have owned work through production, have built something with LLMs or agents, can design securely and for scale, and want to grow into leading designs and mentoring others.

Minimum Qualifications

B.S. in Computer Science, Computer Engineering, or a related field, or equivalent professional experience
5+ years of professional software development, with a track record of owning features or integrations through production
Full-stack experience: a backend language such as Java (Spring Boot or similar) and a modern web UI framework such as React or Angular
Experience designing features with security principles (threat modeling, authentication and authorization, least privilege, secure data handling) and scalable patterns (stateless services, caching, asynchronous messaging, retries, circuit breakers, back-pressure), and explaining your trade-offs in design reviews
Have built an LLM- or agent-based feature, whether in production, as a prototype, or as a significant project, and can describe how you designed it and evaluated its reliability
Strong fundamentals in data structures, design patterns, REST API design, and relational databases
Hands-on observability experience: instrumenting applications with metrics, logs, and traces, and building dashboards and alerts
A record of learning new technologies quickly, working through ambiguity, and communicating clearly across teams

Preferred Qualifications

Shipped agentic features to production, including tool integrations, retrieval pipelines, evaluation, and guardrails

Python, including LLM APIs or agent frameworks
Daily use of GenAI coding tools (Claude, Copilot, Codex) to speed up development, testing, and debugging
Schema design, query optimization, and performance tuning on PostgreSQL, Oracle, or MySQL
Data modeling and reconciliation across multiple sources, including data-quality measurement
Web accessibility (WCAG) and building accessible enterprise UIs
CI/CD, Docker, Kubernetes, and cloud or virtualization platforms
Secure software practices: security design reviews, dependency and vulnerability management, compliance
Experience leading a project or mentoring other engineers, and contributing to inner-source or open-source projects