This AI Engineer role focuses on building generative AI and LLM-powered applications at a mid level of scope and responsibility. The work centers on developing practical LLM solutions, shaping prompt strategies, and implementing retrieval-augmented generation approaches that support reliable application behavior. This role offers the opportunity to contribute directly to applied AI development in a hands-on engineering capacity.
• LLM application development — target L6 (Prof), must-have • prompt engineering, — target L6 (Prof), must-have • retrieval-augmented generation (RAG) — target L6 (Prof), must-have • vector databases — target L6 (Prof), nice-to-have • vector databases — target L6 (Prof), nice-to-have • embeddings — target L6 (Prof), nice-to-have • agentic workflow design — target L6 (Prof), nice-to-have • Working AI leverage — observed and scored, never assumed • 2–8 years of relevant experience
Design, build, and improve LLM-based applications using established approaches in LLM application development. Develop and refine prompt engineering patterns to support application quality and consistency. Implement and support retrieval-augmented generation workflows as part of end-to-end AI application behavior. Contribute to solution design involving embeddings, vector databases, and agentic workflow design where useful and applicable.
this position is for US based clients.
This role provides the chance to work directly on applied generative AI use cases with a clear focus on LLM application development, prompt engineering, and RAG. It is well suited to someone who wants to contribute hands-on to real AI product development while building on adjacent areas such as embeddings, vector databases, and agentic workflow design.
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