Employment: Full-time, direct hire
Location: Remote — United States or Latin America
US compensation: $170,000–$230,000+ annual base, depending on experience and scope. Flexibility toward the higher end for the right staff-level engineer.
LATAM compensation: A separate, lower regional range applies and will be discussed during the recruiting process.
ABOUT OUR CLIENT
Our client builds AI-powered software for healthcare staffing. Its platform helps recruiters find healthcare providers, personalize outreach, manage ongoing conversations, and connect interested providers with relevant opportunities.
ABOUT THE ROLE
Our client is looking for an AI Engineer to build and operate production agent systems, with a focus on agent orchestration, search, retrieval, and evaluation.
You’ll develop the backend systems that allow agents to use tools, retrieve relevant information, maintain state, and complete workflows reliably. You should be comfortable taking systems from implementation through production operation and using repeatable evaluations to determine whether changes actually improve results.
WHAT YOU’LL DO
- Build and improve production LLM agents and the orchestration layer that controls their execution.
- Design tool-calling systems, including tool interfaces, validation, error handling, and recovery.
- Develop retrieval pipelines combining structured filters, keyword search, embeddings, and reranking.
- Build repeatable evaluations and experiments to measure agent behavior, retrieval quality, and regressions.
- Implement durable, asynchronous workflows that handle retries, interruptions, and long-running tasks.
- Investigate production failures and improve reliability, latency, cost, and observability.
- Apply strong backend engineering practices to AI features used in real recruiting workflows.
WHAT WE’RE LOOKING FOR
- Hands-on experience building, deploying, and operating LLM agent systems used in production.
- Strong backend engineering fundamentals, with TypeScript experience or proficiency in an equivalent backend language.
- Practical experience designing agent orchestration or harnesses, including execution control, state management, and tool use.
- Experience with RAG or search systems and an understanding of how filtering, keyword retrieval, embeddings, and reranking work together.
- Experience building repeatable evaluations, comparing approaches, and identifying meaningful improvements.
- Experience with asynchronous processing and reliable workflows.
- Ability to explain architectural decisions, production failures, and the trade-offs behind your solutions.
NICE TO HAVE
- Experience in healthcare, staffing, recruiting technology, or related workflows.
- Staff-level technical ownership across architecture, delivery, and production operation.
- Experience improving both retrieval quality and downstream agent task completion.
INTERVIEW PROCESS
Resume review → G2i video screen for external applicants → direct technical fit review by Fetch → interviews focused on production agent systems, orchestration, tooling, and evaluation.
Target start: Before December.
Employment: Full-time, direct hire
Location: Remote — United States or Latin America
US compensation: $170,000–$230,000+ annual base, depending on experience and scope. Flexibility toward the higher end for the right staff-level engineer.
LATAM compensation: A separate, lower regional range applies and will be discussed during the recruiting process.
ABOUT OUR CLIENT
Our client builds AI-powered software for healthcare staffing. Its platform helps recruiters find healthcare providers, personalize outreach, manage ongoing conversations, and connect interested providers with relevant opportunities.
ABOUT THE ROLE
Our client is looking for an AI Engineer to build and operate production agent systems, with a focus on agent orchestration, search, retrieval, and evaluation.
You’ll develop the backend systems that allow agents to use tools, retrieve relevant information, maintain state, and complete workflows reliably. You should be comfortable taking systems from implementation through production operation and using repeatable evaluations to determine whether changes actually improve results.
WHAT YOU’LL DO
- Build and improve production LLM agents and the orchestration layer that controls their execution.
- Design tool-calling systems, including tool interfaces, validation, error handling, and recovery.
- Develop retrieval pipelines combining structured filters, keyword search, embeddings, and reranking.
- Build repeatable evaluations and experiments to measure agent behavior, retrieval quality, and regressions.
- Implement durable, asynchronous workflows that handle retries, interruptions, and long-running tasks.
- Investigate production failures and improve reliability, latency, cost, and observability.
- Apply strong backend engineering practices to AI features used in real recruiting workflows.
WHAT WE’RE LOOKING FOR
- Hands-on experience building, deploying, and operating LLM agent systems used in production.
- Strong backend engineering fundamentals, with TypeScript experience or proficiency in an equivalent backend language.
- Practical experience designing agent orchestration or harnesses, including execution control, state management, and tool use.
- Experience with RAG or search systems and an understanding of how filtering, keyword retrieval, embeddings, and reranking work together.
- Experience building repeatable evaluations, comparing approaches, and identifying meaningful improvements.
- Experience with asynchronous processing and reliable workflows.
- Ability to explain architectural decisions, production failures, and the trade-offs behind your solutions.
NICE TO HAVE
- Experience in healthcare, staffing, recruiting technology, or related workflows.
- Staff-level technical ownership across architecture, delivery, and production operation.
- Experience improving both retrieval quality and downstream agent task completion.
INTERVIEW PROCESS
Resume review → G2i video screen for external applicants → direct technical fit review by Fetch → interviews focused on production agent systems, orchestration, tooling, and evaluation.
Target start: Before December.