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Clera

Forward Deployment Engineer

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

About this role

Join a fast-growing AI phone automation platform as a Forward Deployment Engineer, bridging product and customer implementations. You'll own end-to-end deployments of AI assistants, integrations, and production rollouts while feeding learnings back into the product for a small, high-velocity team.

What you'll do

  • Translate customer requirements into technical implementation plans and lead production AI assistant rollouts
  • Build integrations with third-party systems (CRMs, calendars, ticketing, payments) via APIs and webhooks
  • Develop glue code and small-to-medium features with testing, monitoring, and logging
  • Debug and deploy rapid fixes for live production issues across calls, flows, and data
  • Create reusable deployment templates, playbooks, and best practices to scale customer setups
  • Surface product feedback and pain points with concrete recommendations to the team

What they're looking for

  • LLM-based application development (prompting, tool use, RAG, failure modes)
  • API and webhook integration with OAuth and API key authentication
  • Production debugging and cloud infrastructure (deployments, observability)
  • TypeScript or Python for glue code and feature development
  • Customer requirements capture and translation to technical specs
  • German language proficiency (B2 level minimum)
  • SQL and database fundamentals
  • Testing, monitoring, and logging practices

Benefits

  • Fully remote position based in Germany or Austria
  • High-impact work shipping to real customers in production
  • Fast-moving team with autonomy and learning opportunities
  • Direct feedback loop into product development
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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

  • Describe your experience deploying and configuring LLM-based applications in production—what challenges have you encountered with prompting or RAG systems?
  • Walk us through a complex third-party API integration you've built; how did you handle authentication and error handling?
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