Software Development Engineer in Test

About Us

We are an early-stage AI startup focused on Site Reliability Engineering (SRE). Rather than being another observability platform, its goal is to act as an AI SRE teammate that works alongside SRE, DevOps, Platform Engineering, Cloud Operations, and IT Operations teams to investigate incidents, determine root causes, and automate remediation.

Our team includes experienced entrepreneurs and engineers who have built multiple billion-dollar products from scratch. As a well-funded US-based company backed by top-tier VCs, we have offices in the US, India, and Europe. Join us in our fast-paced environment where you’ll have a front-row seat to shape the future of AI-driven Observability solutions.

Role Overview

We are looking for a QA / Automation Engineer having 4-8 years of industry experience who operates at the intersection of reliability engineering and system-level QA. You will define how reliability is validated, not just monitored. This includes building automation systems that continuously test, break, and verify complex distributed systems in production-like environments.

What You’ll Work On

• Kubernetes-based distributed systems at scale

• Observability and alerting pipelines

• AI-assisted incident investigation systems

• Multi-cloud (AWS, Azure, GCP) environments, Networking Deployments

• Reliability validation, chaos testing, and failure injection systems

• Infrastructure and deployment automation pipelines

Key Responsibilities

• Define and own the strategy for system-level validation

• Design and build scalable automation frameworks for:

• API, integration, and end-to-end testing

• Kubernetes and system-level validation

• Regression and reliability pipelines

• Build systems that proactively detect failures before they reach production

• Drive chaos engineering and failure injection practices

• Establish CI/CD reliability gates with strong validation coverage

• Partner with SRE, platform, and backend teams to ensure systems are both observable and testable

• Lead incident analysis with a focus on improving validation and preventing recurrence

• Mentor engineers and raise the bar for system reliability and quality

Technical Expectations

Deep Expertise In

• Kubernetes internals, debugging, and multi-cluster systems

• Distributed systems behavior and failure modes

• Observability stacks and alerting frameworks

• Production incident handling and root cause analysis

Strong Hands-on Experience With

• EKS, GKE, or managed Kubernetes platforms

• Networking concepts: VPC, load balancers, service communication, IAM

• Chaos testing and reliability engineering practices

• Designing large-scale automation and validation systems

Programming

• Strong coding skills in Python and Go (mandatory)

• Experience building automation frameworks and system-level tooling

• Proficiency in Shell scripting and infrastructure automation

What Makes This Role Different

• You are responsible for ensuring systems are provably reliable, not just operational

• Deep QA and validation engineering

• Focus on testing distributed systems, not just application features

• Work on failure scenarios, not just happy paths

What Success Looks Like

• A robust validation layer that continuously tests system reliability

• Significant reduction in production incidents and faster recovery times

• Strong alignment between observability signals and real system behavior

• Clear ownership of both reliability and quality across the platform

Scope

• Own platform-wide reliability and validation architecture

• Drive cross-team initiatives across SRE, platform, and engineering

• Act as a technical leader in reliability, automation, and system validation