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.