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Clera

AI Engineer (Mid-Level)

San Francisco$180k–$400kfulltimemidAdded yesterday

About this role

Mid-level AI Engineer role at an early-stage AI startup building agentic systems for enterprise automation across regulated industries. You'll design and deploy production LLM services, RAG pipelines, and multi-agent workflows while owning the full product lifecycle from MVP to enterprise scale.

What you'll do

  • Design and build agentic systems automating multi-step workflows in healthcare, legal, fintech, and compliance domains
  • Own production RAG pipelines, vector databases, embeddings, and retrieval infrastructure at scale
  • Implement multi-agent orchestration, tool-calling, memory, and reasoning components
  • Develop evaluation and safety infrastructure to measure performance and enforce reliability
  • Ship full-stack AI products covering API design, backend/frontend code, CI/CD, monitoring, and testing
  • Collaborate with product and design to define metrics, prioritize work, and iterate on user feedback

What they're looking for

  • LLM deployment and prompt engineering
  • Python and TypeScript/React development
  • RAG patterns and vector databases
  • Multi-agent orchestration frameworks (LangGraph, CrewAI)
  • AWS or GCP cloud platforms
  • Production testing, evaluation, and monitoring for AI systems
  • Workflow orchestration tools (Temporal, Trigger)
  • Enterprise and multi-tenant system design

Benefits

  • Equity compensation
  • Working on cutting-edge agentic AI systems
  • Direct impact with founding team
  • Full ownership of product decisions
  • On-site collaboration in San Francisco
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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 LLM-based services in production—what were the biggest challenges you faced with prompt design or orchestration?
  • Walk us through how you've built and optimized a RAG pipeline for a specific use case; what metrics did you track?
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