David Scheier
Team Lead, Innovation Engineering @Nice Cognigy
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WORK HISTORY
Team Lead, Innovation Engineering @Nice Cognigy
Düsseldorf, DE
Lead the Innovation Engineering portfolio with a strong 0→1 mandate, with people leadership and scope ownership across initiatives.• Product Council member (CEO + Eng/Product leads): cross-functional stakeholder group that sets product strategy and makes go/no-go decisions on high-impact features.• Internal advisor on LLM integration & prompt design, supporting teams on architecture, evaluation, and adoption of new capabilities.• Team leadership: coach and develop engineers, run design reviews, and set the bar for LLM/AI reliability, security, and performance.• Hands-on where it matters: prototype critical paths, author reference implementations, and review high-risk PRs to land production-ready v1s.• Portfolio & prioritization: select bets, sequence 0→1 work, manage dependencies/risks, and align executives and stakeholders.• Standards & platforms: own reference architectures, APIs, guardrails, and evaluation criteria for agentic AI and broader LLM/AI implementations; drive consistent adoption.• Scale enablement (1→N): define patterns, documentation, and handoffs; track adoption/quality and intervene when needed.• Customer/field loop: bring real feedback into the roadmap; measure outcomes and iterate.Recent outcomes:• Formalized LLM/AI standards (tool-use safety, eval metrics, observability) adopted by multiple teams.• Drove cross-team rollout plans for Agentic AI (v1) and MCP tooling; established readiness checklists and go-live gates.• Advised key product squads to unblock high-impact LLM features and reduce time-to-production.
SKILLS
ABOUT DAVID SCHEIER
I lead Innovation Engineering at NiCE Cognigy, partnering closely with our CEO and engineering leadership and serving on the Product Council—a cross-functional stakeholder group that sets strategy across products and makes go/no-go decisions on high-impact features.My work is primarily 0→1: incubating agentic-AI capabilities and broader LLM/AI implementations (LLM orchestration, tool use via MCP, prompt design, streaming/token-by-token outputs, multimodal/image understanding), validating with customers, and landing production-ready v1s. I then enable 1→N by defining patterns, guardrails, and handoff docs so product teams can scale confidently.Recent highlights include our Agentic AI platform (tooling + orchestration), MCP tool integration for governed extensibility, and LLM-native rendering patterns for modern, streaming chat UX. Earlier, I led Webchat 3 from project leadership through technical delivery.
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