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Clera

AI Engineer (Mid-Level)

San Francisco$180k–$400kfulltimemidAdded today

About this role

Mid-level AI Engineer role building production agentic systems that automate complex workflows in regulated domains like healthcare and fintech. You'll own the full stack from LLM orchestration and RAG infrastructure to deployment, working closely with founders on product direction and enterprise reliability.

What you'll do

  • Design and build agentic systems automating multi-step workflows across 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, safety, and monitoring infrastructure to measure performance and enforce reliability
  • Ship full-stack products from MVP to production, including APIs, data models, frontend/backend code, and CI/CD
  • Collaborate with founders, product, and design to prioritize work and iterate on user feedback

What they're looking for

  • Python and TypeScript/React or equivalent
  • LLM deployment and production orchestration
  • RAG patterns, vector databases, and embeddings
  • AWS or GCP cloud platforms
  • Relational and NoSQL databases
  • AI evaluation and testing frameworks
  • API design and high-throughput systems
  • Agent frameworks (LangGraph, CrewAI) and workflow orchestration tools

Benefits

  • Direct influence on product direction and user impact
  • Work across full-stack AI product development
  • Early-stage startup environment with founders
  • Focus on enterprise and regulated industry domains
  • Competitive salary with equity upside
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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 and the key challenges you encountered.
  • Walk us through how you would design and implement a RAG pipeline for a specific use case in a regulated domain.
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