Own core services in TypeScript/Node.js and Python that orchestrate LiveKit, Temporal, STT/TTS, and LLM tooling for real-time voice agents.
Scale 1 → N → 100×: take what works today and harden it for 10K parallel calls with 99.99% uptime. Turn human playbooks into productized systems.
Harden pipelines for ingestion, evaluation, and analytics so telephony events, recordings, and outcomes propagate reliably across services.
Level-up observability: deepen OpenTelemetry/SigNoz and trace-first practices to shrink mean-time-to-truth in prod.
Prototype → test → prod: partner with product to ship new LLM-driven behaviors with clear success metrics, guardrails, and regressions blocked in CI.
Infrastructure readiness: CI/CD, environment automation, incident response playbooks—customer conversations stay online.

Have senior/staff experience running distributed backends with real-time/streaming constraints.
Are fluent in TypeScript/Node.js and comfortable jumping into Python for ML/audio jobs.
Know Temporal (or similar workflow engines), queues, Redis, and PostgreSQL.
Have shipped production LLM apps and understand prompt/tool design, evals, and guardrail instrumentation.
Operate cloud-native on AWS with Terraform; k8s doesn’t scare you.
Are a power user of Cursor/Zed/Devin and were using code-gen before it was cool.
Have intuition for what current-gen LLMs can/can’t do—and what tomorrow’s models will unlock.
Think independently, grind with customers, and do whatever it takes—without dropping the quality bar.
Bonus: built 0→1 real-time systems in Telecom/Networking, Autonomous Vehicles, or HFT; founded something; built AI voice apps.

Senior/Staff Backend Engineer

Key skills