About this Senior QA / SDET Engineer role at 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:
- Automation-first quality engineering for a platform combining voice, desktop, intelligence, and AI.
- We emphasize AUTOMATION — we do not do manual QA here.
- Weekly code deploys are the heartbeat: when a QA branch is ready, regressions run fast, results report fast, and the train doesn't wait.
- Startup environment: 1-week sprints, fail fast, move forward.
What you'll own:
- Automation coverage for your squads' services
- Fast-turnaround regression suites gating weekly deploys ·
- Rapid detection and reporting
- Quality signal in minutes, not days
- Load/stress coverage for your services' latency budgets
- Your committed timelines.
Who you are:
- Self-starter with grit and a show-me mentality.
- You consider yourself exceptional, love new technology, adapt fast when the stack changes under you, and use AI tools daily to multiply velocity — including AI-assisted test generation.
- A team player who likes winning, and a partner to dev, not a gate: when something breaks, you help the dev team isolate it fast.
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5+ years SDET; writes real automation code (Go / Python), not manual scripts. Automation-first is a conviction, not a preference.
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Master issue-isolator and root-cause debugger — you narrow a failure to the service, the commit, the event; dev teams love your bug reports because they're half the debugging done.
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Fast regression discipline — parallelized suites, smart test selection, results in minutes; quality at weekly-deploy speed without becoming the bottleneck.
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Comfortable in high-load testing environments — load, stress, and soak testing of high-throughput, low-latency systems; you know how to find the knee of the curve.
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Experience testing event-driven systems, WebSocket flows, and browser extensions — ordering, race conditions, real-time state.
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CI-native mindset — tests wired into trunk-based CI/CD; flake management as a first-class discipline (a flaky suite is a broken suite).
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Contract/API testing between services; synthetic data and traffic generation (simulated calls, event streams, audio) for repeatable real-time testing.
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Testing AI outputs — eval-style assertions for non-deterministic LLM/ML features — a strong plus.
