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.
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.
Good networking understanding — protocols and how they work (TCP/UDP, TLS, WebSocket, DNS, load balancing); RTP/SIP a strong plus for our media paths.
AI-aware reliability — monitoring model latency, drift, and cost as production signals, not just CPU and memory.
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:
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.
Good networking understanding — protocols and how they work (TCP/UDP, TLS, WebSocket, DNS, load balancing); RTP/SIP a strong plus for our media paths.
AI-aware reliability — monitoring model latency, drift, and cost as production signals, not just CPU and memory.
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.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
