[MLA] Senior Site Reliability Engineer - AI Experience Framework

Software Mind develops solutions that make an impact for companies around the globe. Tech giants & unicorns, transformative projects, emerging technologies and limitless opportunities – these are a few words that describe an average day for us. Building cross-functional engineering teams that take ownership and crave more means we’re always on the lookout for talented people who bring passion and creativity to every project. Our culture embraces openness, acts with respect, shows grit & guts and combines employment with enjoyment. Project – the aim you'll have We are the AI Experience Framework team that builds the platform powering ServiceNow's AI-first user interfaces - an SSR runtime (karuna) built on Lit and server-rendered web components, running behind a multi-tier proxy/HTTP2 routing chain with sharded V8 isolate pools, paired with a ServiceNow Glide/Java platform layer (karuna-glide) that supplies metadata, ACLs, and service artifacts. This role owns production reliability for that stack end to end: Kubernetes deployment and operations, observability, and hands-on troubleshooting of both the Node.js and JVM sides of the system - not generalist infrastructure work. Position – how you’ll contribute Own Kubernetes deployment and operational health for Framework services, including scaling, rollout/rollback strategy, and resource tuning Build and maintain production observability - Grafana dashboards and Prometheus alerting rules - across the SSR runtime and the Glide platform layer Diagnose and resolve Node.js production incidents: event-loop stalls, heap growth, V8 isolate exhaustion, and isolate-pool scheduling issues under concurrent versioned traffic (vN/vN-1) Diagnose and resolve JVM production incidents on the Glide/Java side: GC pressure, thread dumps, and platform-service latency Own incident response for the team: runbooks, on-call rotation, postmortems, and paging hygiene Drive CI/CD and infrastructure-as-code for Kubernetes manifests/Helm and deployment pipelines Partner with the framework engineering team to identify reliability gaps before they become incidents - capacity planning, load testing, chaos/failure-injection where useful Represent production reliability concerns in architecture reviews for new framework capabilities Expectations – the experience you need Production operations/SRE experience, including hands-on Kubernetes deployment, scaling, and incident response Direct operational experience troubleshooting Node.js in production: reading heap snapshots and CPU profiles, diagnosing event-loop blocking, and understanding process/worker isolation models (V8 isolates or equivalent sandboxing) Direct operational experience troubleshooting JVM-based services in production: GC log analysis, thread dump analysis, and JVM tuning Hands-on experience building and maintaining Prometheus alerting rules and Grafana dashboards from scratch, not just consuming existing ones Strong Linux/networking fundamentals: DNS, load balancing, TCP/HTTP semantics (including HTTP/2), and debugging service-to-service networking inside Kubernetes Experience with CI/CD and infrastructure-as-code for containerized deployments (Helm, GitOps tooling such as ArgoCD/Flux, or equivalent) Track record owning on-call rotations, writing runbooks, and driving postmortems that lead to real reliability improvements Experience with Splunk for log aggregation, search, and production troubleshooting. Hands-on experience with in-memory caching systems (Valkey/Redis) in production — key design, TTL/eviction tuning, and tenant-scoped cache invalidation Experience managing service-to-service mTLS - certificate issuance, rotation, and format conversion (e.g., PKCS/BCFKS↔PEM) - plus JWT-based service authentication Very good spoken and written English. Additional skills – the edge you have Familiarity with server-side rendering architectures and the specific failure modes of isomorphic runtimes (markup mismatches, browser-API leakage into server code) Experience operating multi-version/canary rollout strategies (two live app versions served concurrently) Working knowledge of the ServiceNow Glide platform or a comparable enterprise platform integration layer Experience with distributed tracing and request-context correlation across service boundaries Familiarity with event-driven autoscaling (e.g., KEDA ScaledObjects driven by PromQL triggers) as a complement to standard HPA-based scaling Our offer – professional development, personal growth: Flexible employment and remote work International projects with leading global clients International business trips Non-corporate atmosphere Language classes Internal & external training Private healthcare and insurance Multisport card Well-being initiatives Position at: Software Mind