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
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.
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.
Master debuggers and problem solvers. You love solving complex problems, you think outside the box, and you multitask across domains without losing the thread.
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.
Distributed-systems literate. High-efficiency, event-driven, low-latency systems are our world; you understand what that demands.
Innovators who ship. You demonstrate ideas easily, fail fast, and move forward. You make committed timelines and hit them.
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:
- 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.
- 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.
- 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.
The role:
What you'll own:
Who you are:
What You Will Bring:
5+ years SDET; writes real automation code (Go / Python), not manual scripts. Automation-first is a conviction, not a preference.
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.
Fast regression discipline — parallelized suites, smart test selection, results in minutes; quality at weekly-deploy speed without becoming the bottleneck.
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.
Experience testing event-driven systems, WebSocket flows, and browser extensions — ordering, race conditions, real-time state.
CI-native mindset — tests wired into trunk-based CI/CD; flake management as a first-class discipline (a flaky suite is a broken suite).
Contract/API testing between services; synthetic data and traffic generation (simulated calls, event streams, audio) for repeatable real-time testing.
Testing AI outputs — eval-style assertions for non-deterministic LLM/ML features — a strong plus.
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