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LLM Application Engineer

Bjakcareer · United States

remoteunknownPosted Sep 25, 2026PythonLLM APIsAgent frameworksVector databasesPyTorchJAXOpenAI

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About this role

**LLM Application Engineer** **About ActAI** ActAI is building proactive, AI-native applications for the 5 billion users of basic tools like email, notes, tasks, and calendars. The mission is to bring intelligence to conversations, errands, organizing, and workflows with minimal prompting—focusing on high reliability, persistent context, and real-world task completion. **About the Role** As an LLM Application Engineer, you'll build the intelligence layer powering ActAI's AI experiences. You'll work at the intersection of LLMs, software engineering, and product—designing agent workflows, improving model behavior, and turning AI capabilities into reliable user experiences. You'll own problems end-to-end, from understanding user needs to shipping production systems. **Key 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 model outputs into predictable, observable actions • Integrate LLMs with APIs, databases, search, and external tools • Develop prompting, context engineering, and structured outputs to improve model behavior • Build evaluation frameworks and datasets to measure AI quality and reliability • Debug AI systems across the entire stack • Optimize for quality, latency, and cost • Collaborate with product and engineering teams on AI solutions **Tech Stack** Python • LLM APIs (OpenAI-compatible, open-weight models) • Agent frameworks • Vector databases • Backend services • PyTorch/JAX **Ideal Experience** • Strong software engineering fundamentals with AI-powered application experience • Hands-on experience with LLMs, generative AI, or agent-based systems • Experience designing prompts, workflows, evaluations, or AI behavior • Production-quality code writing ability • Comfortable working across abstraction layers • Strong problem-solving in ambiguous, fast-moving environments • Bias toward shipping and continuous improvement

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