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Clera

Forward Deployment Engineer

remote (Remote)$60k–$90kfulltimemidAdded 3 days ago

About this role

Technical role bridging product and customer deployments for an AI phone assistant platform. You'll own end-to-end rollouts of AI configurations, integrations, and production systems while feeding real-world insights back into the product roadmap.

What you'll do

  • Lead end-to-end rollouts of AI assistant configurations, workflows, and guardrails for customer deployments
  • Implement third-party integrations via APIs, webhooks, and services like CRMs, calendars, and payment systems
  • Develop glue code and small-to-medium features to map customer use cases to production systems with testing and monitoring
  • Debug and rapidly fix live production issues across calls, flows, and data pipelines
  • Build reusable deployment templates, playbooks, and best practices to scale customer setups
  • Surface product gaps and recurring customer pain points with actionable recommendations

What they're looking for

  • LLM application development (prompting, tool use, RAG, failure modes)
  • API design and integration (OAuth, API keys, webhooks)
  • Python or TypeScript
  • Production debugging and cloud deployment
  • Observability and monitoring tools
  • SQL
  • Requirements gathering and translation to technical specs
  • GDPR and data privacy (bonus)

Benefits

  • Fully remote, Germany-based
  • Direct impact on product roadmap through customer feedback
  • Work with cutting-edge AI assistant technology
  • Fast-paced, delivery-focused environment
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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 translating customer requirements into production implementations—what challenges have you encountered?
  • Tell us about a complex LLM-based system you've built or deployed. How did you handle failure modes or unexpected behavior?
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