Senior Engineer II - SRE (AI-First)

What you will be working on

• Reliability of a multi-tenant platform under growth: Defining SLIs and SLOs for the services in your area, instrumenting them honestly, and having the error-budget conversation when one is at risk.
• Making releases boring: Progressive delivery through Argo CD — canary and blue/green, automated verification, fast rollback — so a bad change is contained in minutes rather than escalated.
• Release management at pace: Coordinating what ships and when across [N] services, with change gates that catch problems without becoming a queue.
• Helm as a product, not a pile of charts: Reusable, tested, versioned charts that other teams can depend on.
• Incident response and what happens after: Leading incidents on the platform, then writing the postmortem and landing the follow-up work — the part most teams skip.
• Removing toil rather than absorbing it: Every recurring manual task is a candidate for automation in Python, Bash, or Ansible, and you'll have the time budget to actually do it.
• Deciding what AI should and shouldn't touch in our delivery path: Reviewing AI-authored Helm and pipeline code before it reaches production, keeping secrets and customer data out of agent context, scoping the credentials our CI agents hold.

Roles & Responsibilities

• Service level management: Define and operate SLIs, SLOs, and error budgets for platform services; make reliability legible to engineering and product; drive the follow-through when budgets are burned.
• Production on-call and incident response: Hold primary on-call, act as incident commander, and run blameless postmortems with tracked actions. Escalate cleanly to Infra SecOps where infrastructure or credential changes are required. Use AI-assisted analysis to improve triage, root-cause identification, anomaly detection, and MTTR.
• Observability: Build and maintain the instrumentation, SLO monitors, and dashboards in Datadog that make failure visible before customers see it; reduce alert noise as deliberately as you add alerts.
• CI/CD engineering: Design, operate, and optimise build and deployment pipelines — caching, parallel execution, automated testing, security scanning, deployment verification. Manage artifacts and dependencies in the Artifactory.
• Release management and progressive delivery: Own the release process end-to-end; implement GitOps with Argo CD including canary and blue/green rollouts, automated validation, and rollback; run change management so that shipping is routine rather than an event.
• Helm and deployment configuration: Build and maintain production-grade Helm charts and libraries with testing, versioning, policy validation, and reusable standards across services.
• Kubernetes day-2 operations: Operate and troubleshoot production Kubernetes — scheduling, autoscaling, resource management, add-on lifecycle, workload networking — at a depth that holds up at 3am. Cluster provisioning sits with Infra SecOps; you'll partner with them on upgrades and capacity changes.
• Toil reduction and automation: Identify and eliminate recurring operational work with automation in Python, Bash, and Ansible.
• Capacity and resilience: Workload-level capacity planning and resource tuning, resilience and failover testing, and feeding infrastructure capacity requirements into Infra SecOps ahead of need.
• AI guardrails and review: Define how AI-generated deployment and pipeline code is reviewed before it reaches production, keep secrets and customer data out of prompts and agent context, scope CI agent credentials to least privilege, and maintain provenance for generated code.
• Cross-team partnership: Review and raise pull requests against Infra SecOps' Terraform where platform changes are needed, and keep the interface between the two teams working.
• Technical leadership: Contribute to design and architecture reviews, mentor engineers, and raise the bar on availability, deployment speed, and developer experience.

Mandatory Skills

• Production on-call ownership: You've held the pager for a SaaS platform, led incidents end-to-end, and written the postmortems.
• SLO practice, not SLO vocabulary: You've defined SLIs and SLOs for real services and can describe a decision and an error budget changed.
• 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've written charts and libraries that other teams depend on, with tests and CI behind them.
• CI/CD engineering: You've built and owned pipelines at scale, not just consumed them — and you can talk about 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've built the 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 us 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've operated in a model where some remediation needs another team, and you know how to make that fast rather than resentful.