Principal Software Engineer building next-gen AI CX tools for TTEC Digital. Collaborating with teams to solve complex engineering problems in a startup-like environment.
Responsibilities
As this group's Principal Software Engineer, you will be the "right-hand" of the engineering leader and part of the org's leadership team.
You will be the organization's problem solver. Not an architecture title you hide behind — a coder who jumps into whichever squad is stuck, ships the hard thing, and moves on.
You will jointly own the Platform Architect responsibilities for C1 Platform Foundation. This is a fast-moving train in a startup environment: the tech we build on changes monthly, the plan changes with it, and you thrive on that.
Show-me mentality — your influence comes from the code you ship, demoed on Fridays like everyone else.
The hardest problems in any squad · platform architecture decisions (event bus, plugin lifecycle, multi-tenancy, RBAC, schema registry) jointly with the engineering leader
Pattern-setting reference implementations
Training up the engineers around you — you raise the whole team's level · unblocking, by writing code, not memos.
Requirements
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.
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