At TTEC Digital, we coach clients to ensure their employees feel valued, and fully supported, because an amazing customer experience is an employee first process. Our vision is the same, a place where employees know they can thrive. The role: • Own production reliability for a real-time platform where uptime and latency ARE the product — voice, desktop, intelligence, and AI combined; an agent mid-call can't wait for a retry. • First SRE hired immediately (Day 0–14) for production scaling and SLO ownership; a second joins at the start of Phase 3 for 24/7 coverage. • Pairs with C1 Platform Foundation on observability and tenancy isolation. • Startup environment: weekly deploys, 1-week sprints, fail fast, move forward — reliability engineering at that speed, not against it. What you'll own: • SLOs and error budgets per tenant/service • Incident response and blameless postmortems • Production scaling and capacity • Observability depth (p50/p95/p99 per event hop) • Uptime as a personal mission • On-call rotation with DevOps • Your committed timelines. Who you are: • Self-starter, grit, show-me mentality — you prove reliability with dashboards and drills, not assertions. • A ways-to-YES engineer: weekly deploys are the heartbeat and your job is making them safe, never slowing them. • You love new technology, adapt fast when the stack changes under you, use AI tools daily to multiply velocity, and consider yourself exceptional. • Calm in an incident, relentless after it. • Team player who likes winning. • 8+ years operating production systems at scale; owns SLOs, error budgets, incident command. • Strong Go or Python — you automate reliability, you don't toil at it. Everything you build is code: runbooks execute, remediation is automatic, toil trends to zero. • Deep on event-driven and real-time systems reliability — NATS-class buses, WebSocket fleets, streaming pipelines — and the failure physics underneath: state, race conditions, locking, ordering, back-pressure, cascading load. You've debugged these in production. • Strong monitoring and uptime mindset — metrics, logs, traces wired to alerting that catches it before the customer does; you know the difference between a noisy alert and a real signal. • Good networking understanding — protocols and how they work (TCP/UDP, TLS, WebSocket, DNS, load balancing); RTP/SIP a strong plus for our media paths. • GCP at scale; multi-cloud literacy a plus. Multi-tenancy isolation experience a strong plus. • Capacity modeling and load testing partnership with QA — find the knee of the curve before customers do. • Chaos engineering — failure injection as routine practice; prove graceful degradation, don't assume it. • Deploy-safety partnership with DevOps — canary analysis, automatic rollback triggers, error-budget-driven release gates. • AI-aware reliability — monitoring model latency, drift, and cost as production signals, not just CPU and memory. • Incident communication craft — clear, fast, blameless; execs and customers get truth at the right altitude. • A master debugger of production — reads the trace, the metric, the flame graph, and sees it; narrows an incident to the service, the deploy, the event. What You Will Bring: • 8+ years operating production systems at scale; owns SLOs, error budgets, incident command. • Strong Go or Python — you automate reliability, you don't toil at it. Everything you build is code: runbooks execute, remediation is automatic, toil trends to zero. • Deep on event-driven and real-time systems reliability — NATS-class buses, WebSocket fleets, streaming pipelines — and the failure physics underneath: state, race conditions, locking, ordering, back-pressure, cascading load. You've debugged these in production. • Strong monitoring and uptime mindset — metrics, logs, traces wired to alerting that catches it before the customer does; you know the difference between a noisy alert and a real signal. • Good networking understanding — protocols and how they work (TCP/UDP, TLS, WebSocket, DNS, load balancing); RTP/SIP a strong plus for our media paths. • GCP at scale; multi-cloud literacy a plus. Multi-tenancy isolation experience a strong plus. • Capacity modeling and load testing partnership with QA — find the knee of the curve before customers do. • Chaos engineering — failure injection as routine practice; prove graceful degradation, don't assume it. • Deploy-safety partnership with DevOps — canary analysis, automatic rollback triggers, error-budget-driven release gates. • AI-aware reliability — monitoring model latency, drift, and cost as production signals, not just CPU and memory. • Incident communication craft — clear, fast, blameless; execs and customers get truth at the right altitude. • A master debugger of production — reads the trace, the metric, the flame graph, and sees it; narrows an incident to the service, the deploy, the event.