Applied AI Engineer
The posting does not state a salary. This range is our estimate from the role, the location and the stack — treat it as a guide, not an offer.
- Level
- Mid
- Apply from
- United States
About the role
- Expertise in building AI products end to end.
- Strong full-stack engineering with AI focus.
- Experience in mentoring and team growth.
- Ownership of complex AI projects from start to finish.
- Passion for customer insights to shape products.
Skills the posting asks for
- AI
- APIs
- databases
- infrastructure
- full-stack
Summarised from the employer’s posting, which is reproduced in full below.
The employer’s full posting
Requirements
- You have at least 4 years of professional software development experience ,
- You possess deep experience building production systems with language models, including agent loops, tool use, retrieval, prompting, and structured outputs ,
- You are comfortable owning production software across frontend, backend, APIs, databases, and infrastructure ,
- You have led complex projects from experimentation through production and thrive when defining solutions rather than following a spec ,
- You have strong product intuition and care about the “why” as much as the “how” when building user-facing software ,
- You’re eager to talk directly with customers and use those insights to shape what we build ,
- You have experience mentoring other engineers, sharing technical knowledge, and helping teammates grow
- We grew in value faster than headcount and we’re looking to align the two quickly ,
- We’re looking for product-minded engineers to build the AI experiences behind Mintlify. You’ll combine strong full-stack engineering with applied AI expertise to create agents that reliably solve real customer problems ,
- As an Applied AI Engineer, you’ll take ownership of complex AI products from early experimentation through production. You’ll help define technical direction, establish how we evaluate and improve agent performance, and raise the quality of both our AI products and engineering practices ,
- Building AI products end to end: Designing and shipping customer-facing AI experiences, from agent orchestration and tool use to the interfaces and backend systems that support them ,
- Developing reliable agents: Building agent loops that reason over complex context, call tools, recover from failures, and complete long-running tasks ,
- Evaluating and improving performance: Creating evals, analyze failures, and hill-climb on prompts, context, models, tools, and product flows ,
- Shipping across the stack: Building the APIs, data pipelines, interfaces, and production infrastructure needed to turn promising prototypes into polished products