Sobre este puesto de Senior DevOps Engineer en Ttecdigital
The work:
We’re an innovation group inside TTEC (NASDAQ: TTEC), building the next generation of AI CX tools — automated QA, conversational analytics, knowledge assist, and agentic automation — for the world’s biggest brands and the millions of customers they serve. We move like an early-stage startup, backed by the scale, distribution, and enterprise client base of a company that’s been obsessed with customer experience since 1982.
This is the rare seat where getting in early actually matters at scale. TTEC is a public company at an AI inflection point. Ship the right products into thousands of live enterprise deployments and you don’t just move a metric — you move the trajectory of the company and the value of the stock. The leverage is real, and the work compounds.
Who we hire — the DNA
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Self-starters and do-ers with grit — hackers in the best sense, with a startup mentality and a show-me bias: working software over slides, prototypes over proposals.
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Want to learn, love new technology. This platform is built on the latest technology, and that technology changes and advances monthly. You adapt to change quickly — new tools, new models, new priorities — without drama.
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Master debuggers and problem solvers. You love solving complex problems, you think outside the box, and you multitask across domains without losing the thread.
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AI-native. You work with AI on all levels — you understand the technology around you (LLMs, SLMs, RAG, knowledge graphs, agents, training, eval) and you use AI tools daily to exponentially increase your velocity.
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Distributed-systems literate. High-efficiency, event-driven, low-latency systems are our world; you understand what that demands.
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Innovators who ship. You demonstrate ideas easily, fail fast, and move forward. You make committed timelines and hit them.
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You consider yourself exceptional — and you like winning. So do we.
No one will have everything in this description. We're looking for well-rounded, smart people who move fast.
What You Will Do:
The role:
- Stand up and run the platform's infrastructure — CI/CD, multi-cloud, observability baselines, the daily-deploy pipeline.
- Two DevOps shared across the org, supporting a real-time platform (voice, desktop, intelligence, AI) where latency and uptime are product features.
- Startup environment: 1-week sprints, weekly deploys to production, fail fast, move forward.
What you'll own:
- CI/CD (trunk-based, daily prod deploys, PR-to-staging)
- Infrastructure-as-code for everything — no hand-built anything ·
- GCP primary, cloud-agnostic deployment templates for multi-cloud
- Observability and monitoring baselines ·
- Uptime as a personal mission ·
- On-call rotation with the SRE
- Your committed timelines.
Who you are:
- A ways-to-YES engineer — when a squad needs something, your default is "here's how we do it safely," never a block.
- Self-starter, grit, show-me mentality: your infra demos too.
- You love new technology, adapt fast, use AI tools daily to multiply velocity, and consider yourself exceptional.
- Team player who likes winning.
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7+ years DevOps / platform; deep GCP plus real multi-cloud experience — you've deployed and run production workloads on at least two clouds and built abstractions that keep us portable.
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Strong INFRA-AS-CODE mentality — Terraform-class IaC is how everything exists; if it isn't in code, it isn't real. Reviewable, repeatable, destroyable, rebuildable.
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Has run infra for a daily-deploy, trunk-based shop; CI/CD for high-cadence teams is muscle memory.
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Codes — Go or Python tooling, not just YAML.
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Good networking understanding — protocols and how they actually work: TCP/UDP, TLS, HTTP/2, WebSocket, DNS, load balancing, and ideally RTP/SIP for our media paths. You debug at the packet level when you have to.
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Strong mindset on monitoring and uptime — metrics, logs, traces, alerting baselines from day one; you notice before the customer does.
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GitOps discipline — desired state in git, drift detection, PR-driven infra change.
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Deploy safety engineering — canary/progressive rollout, feature-flag integration, instant rollback; weekly deploys stay boring.
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Kubernetes/container depth (GKE-class) — scheduling, autoscaling, resource tuning for latency-sensitive services.
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Secrets and least-privilege IAM as defaults; SOC 2-ready posture without slowing the train.
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Cost awareness — you catch the runaway bill in the dashboard, not the invoice; efficiency is part of the job.
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Ephemeral environments — spin up a full stack per PR/demo, tear it down after.
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A debugger of infrastructure — reads the deploy log, the metric spike, the routing table, and sees it.
