Top Tech Jobs & Startup Jobs in Seattle, WA

Own and build Replicant’s revenue operations function, including forecasting, pipeline management, compensation, pricing, quota setting, capacity planning, GTM systems, and AI-assisted reporting. Serve as a strategic partner to Sales and Finance leadership, consolidate fragmented revenue data, improve operating decisions, and grow the RevOps team from one analyst. The role requires experience building RevOps at growth-stage SaaS companies and leading cross-functional revenue strategy.

Lead exploration and application of large language models and generative AI for enterprise contact-center automation. Translate research into high-performing, deployable ML systems; contribute across experimentation, implementation, and deployment; collaborate with engineering and product teams; optimize performance; and help shape team strategy, innovation, and professional growth.

Build and improve Replicant’s AI-native platform infrastructure, including site reliability, CI/CD, developer tooling, observability, incident management, and agent harness systems. Own production infrastructure patterns, deployment workflows, and developer self-service across Kubernetes and cloud environments. Participate in on-call and incident response while improving scalability, availability, and operational efficiency for real-time conversational AI traffic.

Build and lead Replicant’s company-wide AI enablement program. Develop MCP integrations, shared agents, agent identity management, enterprise tool connections, reusable AI workflows, and cost-visibility mechanisms. Partner with Sales, Delivery, Finance, People, Engineering, and other teams to identify opportunities, implement solutions, promote AI literacy, and develop internal champions. Optimize AI systems through model routing, prompt caching, better agent design, and secure infrastructure.

Work with sales and engineering to own the technical pre-sales process: gather requirements, design AI agent integrations, write and review APIs, run hands-on trials using prospect call data, iterate prompts and conversational designs, analyze conversational insights, and present technical findings to business and technical stakeholders.

Design, build, and operate customer-specific AI agents end-to-end: map workflows, write prompts and conversation flows, integrate with customer APIs, create test sets, monitor live conversations, debug production issues, and iterate on agent quality with product feedback.

The Lead Quality Engineer will set quality standards, mentor a QA team, improve testing strategies, and enhance automation in AI-driven workflows.

Build full-stack features for AI voice and chat products using TypeScript, Node.js, React, and Python. Integrate ASR/TTS and LLM improvements, optimize production systems for latency and cost, participate in on-call rotations, and collaborate with ML and product teams to scale enterprise AI conversations.

Lead a Product Engineering squad (8–15 engineers), prioritize people development, hiring, process, and predictable delivery. Drive AI-native practices, technical credibility, customer-focused outcomes, testing/validation, and cross-functional partnerships while owning team budget and headcount.