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.
Who you are:
12+ years hands-on, genuinely polyglot — fluent in 7+ languages across your career. Senior-level Go is mandatory; Python and TypeScript expected in the mix; a native/systems language a strong plus.
Breadth across multiple technologies and verticals — you've shipped in more than one industry and more than one architectural era, and adapted fast each time.
Deep understanding of high-capacity distributed systems and their failure physics — state management, race conditions, locking, contention, ordering, back-pressure. You've debugged these in production, not just read about them.
Multiple messaging systems (NATS / Kafka / Redpanda / RabbitMQ-class) and multiple database types and backgrounds — relational, KV, document, columnar, graph — and you know which fits and why.
Two clouds minimum, one deep; GCP strongly preferred as the deep one.
Strong AI knowledge — LLMs, SLMs, RAG, knowledge graphs, agents, training/fine-tuning, eval — and hands-on experience working AI into code: building AI-powered features and using AI-assisted development to move exponentially faster.
Troubleshooting/debugging master — the strongest debugger in the building. Reads unfamiliar code, a stack trace, or a flame graph and sees it. Knows where the breakpoint goes without flailing.
Problem-solves quickly and trains up others as a multiplier — pattern-matches instantly on distributed systems, real-time audio, browser internals, AI pipelines, high-throughput data models.
Floats by choice. Does not want to own a squad. Wants to own the hard 10%.
Sets patterns by shipping the reference implementation, not by writing the doc.
Set privacy engineering strategy and build critical data-governance systems across products, research, and infrastructure, translating regulatory and consent obligations into technical controls while guiding engineers across teams.
Sets technical direction and owns hands-on engineering for analytics and knowledge management systems, shaping architecture, quality, reliability, and AI-assisted document ingestion and retrieval across backend and frontend services.
Own critical payments initiatives end to end, shaping architecture and technical direction for Brazilian payment systems while delivering reliable production solutions and raising engineering standards.
Lead the technical direction of a high-traffic public web platform, improving rendering, performance, SEO, experimentation, and AI-powered experiences while setting standards and mentoring engineers.
Set technical direction for Flow’s AI agent runtime and delivery practices, building reliable LLM-powered workflows while leading architecture, production quality, and engineering standards across teams.
