Mintlify
Applied AI Engineer
About this role
Mintlify seeks an Applied AI Engineer to build end-to-end AI products for their documentation platform serving 100M+ developers. You'll design and ship customer-facing AI experiences including agents, tool use, and production infrastructure while collaborating with a small, high-impact team.
What you'll do
- Design and ship customer-facing AI experiences including agent orchestration, tool use, and backend systems
- Build reliable agent loops that reason over complex context, handle tool calls, and recover from failures
- Create evaluation frameworks to measure agent performance and iterate on prompts, models, and product flows
- Own production systems across frontend, backend, APIs, databases, and infrastructure
- Drive complex projects from experimentation through production with minimal specification
- Mentor engineers and share technical knowledge across the team
What they're looking for
- Production language model systems (agents, tool use, retrieval, prompting, structured outputs)
- Full-stack software development (frontend, backend, APIs, databases, infrastructure)
- Agent design and orchestration
- Evaluation and optimization of AI systems
- Product intuition and user-focused thinking
- Complex project leadership and ownership
- Mentoring and knowledge sharing
Benefits
- Work on technically ambitious products with a team valuing craft and thoughtful execution
- High agency as a product builder with voice in strategic decisions
- Plenty of heads-down builder time on a small, impactful team
- Opportunity to directly shape product direction through customer insights
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Mintlify
Mintlify builds a documentation platform serving millions of developers globally. The company is hiring Support Engineers to resolve issues at the source through code fixes and automation, product engineers to build full-stack user-facing features, and Solutions Engineers to manage customer migrations and support strategic accounts.
- Website
- mintlify.com
Likely interview questions
- Walk us through a production language model system you've built—what challenges did you face with agent reliability or tool use?
- How do you approach evaluating and improving agent performance? What metrics and iteration strategies have worked best?