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