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Clera

Forward Deployment Engineer

remote (Remote)$60k–$90kfulltimemidAdded today

About this role

Bridge technical requirements between product and customers for an AI voice platform, owning end-to-end deployments of AI assistants with integrations, monitoring, and rapid iteration. You'll translate customer needs into production workflows while feeding insights back to the product team.

What you'll do

  • Own end-to-end production rollouts of AI assistant setups including workflows, prompts, guardrails and data flows
  • Implement integrations with third-party systems via APIs, webhooks and services like CRM, calendar, ticketing and payments
  • Debug live production issues and deploy rapid fixes across calls, flows and data systems
  • Develop small-to-medium features and glue code to map customer use cases to production systems with testing and monitoring
  • Build reusable deployment templates, playbooks and best practices to scale setups
  • Maintain feedback loop to product team by surfacing pain points, gaps and recurring patterns

What they're looking for

  • LLM systems and prompting (tool use, RAG, evaluation, failure modes)
  • TypeScript or Python coding for glue code and features
  • API and webhook integrations with OAuth and API key authentication
  • Production workflow and data flow implementation
  • Cloud deployment and observability/debugging in production
  • Testing, monitoring and logging practices
  • Customer requirements gathering and translation to technical specs
  • German language (B2 level or above)

Benefits

  • High-impact role with direct influence on product development
  • Remote work from Germany or Austria
  • Fast-moving startup environment with ownership opportunities
  • Work on cutting-edge AI voice and communication technology
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Clera

Clera builds an agentic operating system that automates complex workflows and processes through AI agents, with a platform designed to simplify distributed infrastructure management for developers. The company is hiring Founding Engineers, Customer Engineers, and Product Engineers to develop both backend systems and user-facing interfaces across their AI automation products.

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Likely interview questions

  • Walk us through a time you translated complex customer requirements into a production deployment plan—what was the most challenging part?
  • Describe your experience building or rolling out workflows for LLM-based systems. What failure modes did you encounter and how did you handle them?
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