LLM Application Engineer, Artificial Intelligence (AI) Required, Work From Home

LLM Application Engineer, Artificial Intelligence (AI) Required, Work From Home As an LLM Application Engineer, you will build the intelligence layer that powers the AI experiences. You will work at the intersection of Large Language Models (LLMs), software engineering, and product designing agent workflows, improving model behavior, and turning Artificial Intelligence (AI) capabilities into reliable user experiences. You will own problems end-to-end, from understanding user needs, designing Agentic workflows, integrating models and tools, building evaluation systems and continuously improving AI behavior in production. This position is 100% Remote. LLM Application Engineer Responsibilities: - Build and ship LLM-powered applications and AI agent workflows. - Design systems for reasoning, planning, memory, tool use and multi-step execution. - Build reliable orchestration pipelines that turn probabilistic model outputs into predictable, observable, and safe actions. - Integrate Large Language Models (LLMs) with APIs, databases, search, internal services, and external tools. - Develop prompting, context engineering, structured outputs, tool-calling, and other techniques to improve model behavior. - Build evaluation frameworks and datasets to measure AI quality, reliability, and regressions. - Debug AI systems across the entire stack from model behavior and prompts to orchestration, backend services, and product UX. - Optimize AI systems for quality, latency, and cost. - Work closely with product and engineering teams to turn ambiguous product problems into working AI solutions. - Establish production practices for observability, tracing, experimentation, evaluation, and continuous improvement. LLM Application Engineer Outcomes: - Artificial Intelligence (AI) features reach production quickly and deliver measurable user impact. - LLM-powered workflows are reliable, scalable, observable, and maintainable. - Artificial Intelligence (AI) quality improves through systematic