Principal, Software Engineer

About the role

Position Summary... Principal Software Engineer (SRE): Unified Observability Observability

Are you excited by the idea of systems that heal themselves? We're building a unified observability platform that gives 360° visibility across distributed systems, with minimal instrumentation and AI-driven insights that detect, predict, and resolve issues in real time. Our north star is self-healing infrastructure, where AI agents diagnose problems and trigger remediation with little human intervention.

As a principal engineer here, you'll design and deploy scalable AI systems for anomaly detection, forecasting, and root-cause analysis. You'll build real-time inference services and event-driven data pipelines that carry telemetry, logs, metrics, and traces at serious scale. You'll write high-performance Golang backends, architect fault-tolerant multi-cloud systems on Kubernetes, and automate the full MLOps lifecycle. You'll partner closely with SRE and Product teams, shape technical strategy, and mentor engineers who are growing into the next generation of AIOps builders.

If you bring 10–15 years of software experience, hands-on AI Agent deployment from ingestion to monitoring, and a passion for resilient distributed systems, we'd love to meet you. Here, your work keeps millions of customer experiences running smoothly, and your ideas will shape how an entire engineering organization operates. Join us and help build systems that take care of themselves.

What you'll do...

About the team

We are building a unified observability platform that delivers 360° visibility across distributed systems with minimal instrumentation overhead. The platform seamlessly integrates with existing environments and workflows, leveraging AI-driven insights to detect, predict, and resolve issues in real time.

Our goal is to enable self-healing systems through AI agents that autonomously diagnose and trigger remediation actions with minimal human intervention.

What You’ll Do

Core Development

- Design, develop, and deploy scalable AI/ML agents for anomaly detection, forecasting, and root-cause analysis.

- Build and optimize real-time inference APIs and services integrating ML pipelines into production.

- Develop data pipelines for large-scale telemetry, logs, metrics, and traces using event-driven architectures.

- Automate model training, evaluation, and deployment pipelines (MLOps).

- Continuously monitor model performance and optimize for accuracy, latency, and cost.

- Work closely with platform and SRE teams to build AI-powered automation and observability workflows.

Software & Systems Engineering

- Build high-performance backend systems using Golang and modern design patterns.

- Architect distributed and fault-tolerant systems with strong fundamentals in concurrency, scalability, and resilience.

- Design multi-cloud applications using Kubernetes, Docker, and infrastructure-as-code tools.