Software Developer Sr

About the Opportunity
The Test Platform team builds and operates a large-scale, event-driven execution engine that runs quality validation across all of Product and Technology. Our distributed systems orchestrate huge volumes of test and validation work, and we build the tooling, frameworks, and AI capabilities that development teams rely on every day.
As a Lead Developer, you'll own core parts of this platform, from the distributed execution engine and messaging backbone through the AI tooling built on top of it. You'll set technical direction, drive architecture, and help teams across the organization build quality into what they ship.
We run a cloud-native, event-driven execution engine that schedules and runs hundreds of thousands of workloads across a distributed, containerized platform. It runs on Kubernetes, relies on asynchronous messaging and event streaming, and scales horizontally across enterprise SaaS workloads. Think high throughput, fault tolerance, deep observability, and elastic orchestration, with an AI layer on top that makes the platform smarter over time.
What You'll Get to Do
Platform and Distributed Systems Engineering

Design, build, and run the event-driven execution engine that orchestrates test and validation workloads across a Kubernetes-based platform.
Architect for throughput, resiliency, and elasticity using asynchronous messaging, event streaming, and distributed coordination.
Own the platform end to end, including shared frameworks, execution orchestration, test data management, environment provisioning, and self-service tooling that teams depend on.
Build observability into the platform so telemetry from a large distributed system turns into insights people can act on.

AI and Intelligent Automation

Design and maintain AI-powered tools and agents that improve developer productivity, including code generation, intelligent test generation, and automated review.
Evaluate and integrate large language models and AI coding assistants, such as GitHub Copilot and custom agents, into engineering workflows.
Build AI-driven optimization into the engine, including predictive workload selection, flaky detection, intelligent prioritization, and self-healing automation.

Technical Leadership

Set architectural direction for the platform with scalability, reliability, and testability in mind.
Evaluate emerging tools, frameworks, and AI capabilities, build proofs of concept, and drive adoption of the ones that matter.
Mentor other engineers on distributed systems design, modern development practices, and getting real value out of AI-assisted tooling.
Own initiatives from first idea through delivery and measurement.
Drive continuous validation in CI/CD pipelines with smart gating, parallelization, and fast feedback.

Skills and Experience We Value

Experience building or integrating AI coding assistants, copilots, or autonomous agents into engineering workflows, whether through LLM integration, prompt engineering, or AI-powered developer tools.
Experience building and running distributed systems at scale, including event-driven architecture, messaging or streaming systems, and horizontally scalable services.
Hands-on experience with Docker and Kubernetes in production, plus CI/CD automation such as Azure DevOps or GitHub Actions.
Strong proficiency in C#, .NET Core, and SQL Server.
Experience with cloud infrastructure on Azure or AWS.
Experience with modern frontend frameworks such as React or Angular.
Technical depth to lead deep-dive discussions on distributed systems and AI strategy.
Genuine curiosity about emerging technology, especially AI and ML.
Strong debugging, profiling, and performance analysis skills.
Ability to work effectively with distributed teams and communicate clearly.

What Would Make You Stand Out

Experience leading development of complex, cloud-based SaaS applications at scale.
Deep experience with microservices, distributed coordination, and high-throughput workload orchestration.
Experience with contract testing, service virtualization, and test infrastructure such as in-memory databases and test containers.