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Contentstack is an Agentic Experience Platform (AXP) that unifies structured content management, real-time customer data, omnichannel personalization, and autonomous AI orchestration. The company serves leading brands including Steve Madden, LG Electronics, Subaru, Dolce & Gabbana, ASICS, Decathlon, and Caesars Entertainment.
As a Senior Engineer II - SRE (AI-First), you will own the reliability and operational excellence of a multi-tenant SaaS platform at scale. Your focus areas include:
**Core Responsibilities:**
- Define and operate SLIs, SLOs, and error budgets for platform services; drive follow-through when budgets are at risk
- Hold primary on-call rotation, act as incident commander, run blameless postmortems with tracked actions, and use AI-assisted analysis to improve triage and MTTR
- Build and maintain instrumentation, SLO monitors, and dashboards in Datadog; reduce alert noise deliberately
- Design, operate, and optimize CI/CD pipelines including caching, parallel execution, automated testing, security scanning, and deployment verification
- Own the release process end-to-end with GitOps (Argo CD), implementing canary and blue/green rollouts, automated validation, and rollback
- Build and maintain production-grade Helm charts and libraries with testing, versioning, and reusable standards
- Operate and troubleshoot production Kubernetes (EKS) at depth—scheduling, autoscaling, resource management, add-on lifecycle, workload networking
- Identify and eliminate recurring operational work through automation in Python, Bash, and Ansible
- Perform workload-level capacity planning, resilience testing, and feed infrastructure requirements upstream
- Define how AI-generated deployment and pipeline code is reviewed before production; keep secrets and customer data out of agent context; scope CI agent credentials to least privilege
- Partner with Infra SecOps on Terraform changes and maintain a smooth cross-team interface
- Contribute to design and architecture reviews, mentor engineers, and raise the bar on availability, deployment speed, and developer experience
**Requirements:**
- Production on-call ownership: You have held the pager for a SaaS platform, led incidents end-to-end, and written postmortems
- SLO practice, not vocabulary: You have defined SLIs and SLOs for real services and can describe a decision and error budget change
- Kubernetes in production: Deep, hands-on day-2 operation and troubleshooting of EKS—scheduling, autoscaling, resource management, add-ons—not just deploying onto it
- Helm depth: You have written charts and libraries that other teams depend on, with tests and CI behind them
- CI/CD engineering: You have built and owned pipelines at scale, not just consumed them; you can discuss what you did to make them fast and trustworthy
- Release engineering judgement: Practical experience with progressive delivery and rollback (Argo CD, Flux, or equivalent)
- Observability: Hands-on with Datadog or equivalent—you have built monitors and dashboards, not just read them
- Systems fundamentals: Strong Linux, load balancing, and application clustering; you can debug from logs, stack traces, and core dumps
- Demonstrated AI-assisted workflow: Show something you built—an agent or MCP integration, a prompt/context library your team uses, an AI-assisted review or incident workflow—and tell us where you decided not to use it
- Works well across a boundary: You have operated in a model where some remediation needs another team, and you know how to make that fast rather than resentful
