Senior Site Reliability Engineer — Voice AI Platform

Location: Bangalore, India

Department: Technology

Experience: 5+ Years

• SLOs and error budgets. Define and defend service-level objectives for availability and latency across the call path, and use error budgets to steer the balance between shipping and stability.
• Observability. Own the metrics, tracing, and logging stack so failures surface fast and root cause is minutes not hours — distributed traces across signaling, ASR, LLM, TTS, and infra, with dashboards and alerting that page on real problems and stay quiet otherwise.
• The real-time media path. Keep SIP signaling and RTP media healthy at scale — concurrency, jitter, packet loss, session setup — and the reliability of the components that carry them.
• Capacity and autoscaling. Plan for peak (campaign windows that push toward the platform's concurrency ceiling), pre-warm capacity ahead of demand, and tune autoscaling so we neither drop calls nor burn money idling.
• Incident response. Run a calm, structured on-call: triage, mitigation, clear comms to stakeholders on regulated accounts, and blameless postmortems that actually change the system.
• Multi-cloud resilience. Design for failure across AWS, GCP, and Azure — redundancy, failover, disaster recovery, and the data-residency constraints that come with Indian banking and telecom clients.
• Automation and toil reduction. Turn manual operations into infrastructure-as-code and self-healing systems. If you did it twice by hand, the third time is a script.

• First 90 days. Learn the call path end to end. Establish baseline SLIs for availability and latency, close the biggest gaps in alerting, and take a full turn in the on-call rotation.
• By 6 months. Published SLOs with error budgets for the core services. A tracing/dashboards setup that makes the ASR→LLM→TTS latency budget visible per call. A repeatable pre-warm-and-scale playbook for campaign peaks.
• By 12 months. Demonstrable reduction in incident frequency and time-to-mitigate. Tested multi-cloud failover for a critical path. On-call toil measurably down through automation.

• Several years running high-availability, high-throughput production systems, including real on-call ownership.
• Depth in at least one major cloud (AWS, GCP, or Azure) and with containers/Kubernetes.
• Strong observability practice — metrics, distributed tracing, and logging (e.g. Prometheus/Grafana, OpenTelemetry, Tempo/Jaeger).
• Fluency with SLIs/SLOs/error budgets and structured incident management.
• Infrastructure-as-code (Terraform or similar) and CI/CD.
• A programming language for automation and tooling (Python, Go, or similar) — beyond shell scripting.
• Solid Linux systems and networking fundamentals; capacity planning and performance tuning.

• Real-time media or VoIP experience — SIP/RTP, media servers, LiveKit, SBC/Kamailio.
• Reliability of GPU/ML serving infrastructure.
• Regulated-industry operations — uptime SLAs, DR, data residency.
• Load testing and chaos engineering at scale.
• PostgreSQL operations at scale.