Senior Member of Technical Staff - Cloud Infrastructure

About the Role: We are looking for a Senior Engineer to join our Cloud Platform team and contribute to the development, operation, and reliability of our multi-tenant SaaS platform. You will work on backend systems and Cloud infrastructure — building features, fixing real production problems, and growing into broader ownership over time. This is a hands-on role with real ownership. You are expected to take a feature or problem end-to-end, write production-quality code, and work closely with more senior engineers on design and architecture. What You Will Do: Engineering & Development Build and maintain platform components across control plane and data plane Implement features for tenant provisioning, configuration management, and cluster operations Write clean, well-tested, production-grade code and participate actively in code reviews Debug and resolve issues in cloud-native, distributed production environments Operations & Reliability Partner with SRE on observability, alerting, and incident response for services you own Improve reliability and operability of platform systems — reduce toil, improve monitoring, fix recurring issues Contribute to on-call and develop strong instincts for production system behaviour. Collaboration & Growth Work closely with senior and principal engineers on design discussions and RFCs Collaborate with cross-functional teams across geographies Participate in hiring — conduct interviews and contribute feedback. ​ Must Have: Cloud & Infrastructure Solid hands-on experience with at least one of AWS, GCP, or Azure — compute, networking, IAM, and managed services Working knowledge of infrastructure as code — Terraform or equivalent — for provisioning and managing cloud resources. Comfortable working with containerized workloads — Docker, Kubernetes basics (deployments, services, config maps, RBAC, namespaces) ​ Backend Engineering Strong programming skills in at least one backend language — Go, Java, Python, or equivalent Experience building and operating REST or gRPC APIs in production Good understanding of databases — relational and NoSQL — and how to use them reliably at scale Distributed Systems Practical understanding of distributed systems — service dependencies, failure modes, retries, timeouts, and basic HA patterns. Hands-on with observability — structured logging, metrics dashboards (Grafana, Datadog, or equivalent), and basic alerting; able to diagnose production issues using these tools Security Fundamentals Awareness of cloud security basics — IAM least-privilege, secrets management, and network access controls AI-Augmented Engineering Uses AI coding assistants — Claude, Cursor, Copilot — as a regular part of daily workflow for writing, debugging, and reviewing code Comfortable using AI tools to understand unfamiliar codebases, generate boilerplate, draft documentation, and speed up routine tasks Knows to review AI output carefully and apply judgment before committing or deploying Good to Have Exposure to Kubernetes operators, controllers, or CRDsFamiliarity with GitOps workflows — ArgoCD, Flux, or equivalent Basic understanding of multi-tenancy concepts — isolation, resource quotas, or tenant lifecycleExperience contributing to or building internal observability dashboards and alerting pipelines. What Success Looks Like In 3 months: - Ramped up, productive, and shipping features with guidance - Comfortable with the codebase, deployment process, and team workflow - Engaging actively in code reviews and team discussions In 6 months: - Owning features end-to-end with increasing independence - Reliable on-call contributor — able to diagnose and resolve common production issues - Proactively improving the quality and observability of systems you work on In 12 months: - Trusted, high-output contributor on the team - Beginning to mentor interns or junior engineers - Taking on broader problems that span multiple components or systems Mandatory a ... (truncated, view full listing at source)