Site Reliability Engineer (SRE)

TTEC Digital

Job Location:

Hyderabad - Pakistan

Monthly Salary: Not Disclosed

Posted on: 2 days ago

Vacancies: 1 Vacancy

Job Summary

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 cant wait for a retry.
First SRE hired immediately (Day 014) 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 youll 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.

8yearsoperating production systems at scale; owns SLOs error budgets incident command.

Strong Go or Python you automatereliabilityyoudonttoil at it. Everything you build is code: runbooksexecuteremediation is automatic toil trends to zero.

Deep on event-driven and real-time systems reliability NATS-class buses WebSocket fleets streaming pipelines and thefailurephysics underneath: state race conditions locking ordering back-pressure cascading theseinproduction.

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 literacya plus. Multi-tenancy isolationexperiencea strongplus.

Capacity modeling and load testing partnership with QA find the knee of the curve before customers do.

Chaos engineering failure injection as routine practice;provegraceful degradationdontassume 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:

8yearsoperating production systems at scale; owns SLOs error budgets incident command.

Strong Go or Python you automatereliabilityyoudonttoil at it. Everything you build is code: runbooksexecuteremediation is automatic toil trends to zero.

Deep on event-driven and real-time systems reliability NATS-class buses WebSocket fleets streaming pipelines and thefailurephysics underneath: state race conditions locking ordering back-pressure cascading theseinproduction.

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 literacya plus. Multi-tenancy isolationexperiencea strongplus.

Capacity modeling and load testing partnership with QA find the knee of the curve before customers do.

Chaos engineering failure injection as routine practice;provegraceful degradationdontassume 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.

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.

Required Experience:

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 cant wait for a retry.
First SRE hired immediately (Day 014) 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 youll 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.

8yearsoperating production systems at scale; owns SLOs error budgets incident command.

Strong Go or Python you automatereliabilityyoudonttoil at it. Everything you build is code: runbooksexecuteremediation is automatic toil trends to zero.

Deep on event-driven and real-time systems reliability NATS-class buses WebSocket fleets streaming pipelines and thefailurephysics underneath: state race conditions locking ordering back-pressure cascading theseinproduction.

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 literacya plus. Multi-tenancy isolationexperiencea strongplus.

Capacity modeling and load testing partnership with QA find the knee of the curve before customers do.

Chaos engineering failure injection as routine practice;provegraceful degradationdontassume 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:

8yearsoperating production systems at scale; owns SLOs error budgets incident command.

Strong Go or Python you automatereliabilityyoudonttoil at it. Everything you build is code: runbooksexecuteremediation is automatic toil trends to zero.

Deep on event-driven and real-time systems reliability NATS-class buses WebSocket fleets streaming pipelines and thefailurephysics underneath: state race conditions locking ordering back-pressure cascading theseinproduction.

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 literacya plus. Multi-tenancy isolationexperiencea strongplus.

Capacity modeling and load testing partnership with QA find the knee of the curve before customers do.

Chaos engineering failure injection as routine practice;provegraceful degradationdontassume 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.

Required Experience:

Apply Now

About Company

TTEC Digital

We believe that humanity and technology should âco-exist, and that at the nexus of the âmost powerful experiences, humanity and technology collide. At TTEC Digital, we combine the expertise, innovation, partnerships, and passion of three industry-leading customer experience com ... View more

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