This role is designed to connect the dots across ingestion, retrieval and generation ensuring that the full context pipeline is optimized holistically, not just in isolated parts. Contextual AI's context layer technology enables enterprises to move beyond demos to actual production-grade AI applications. However, getting context layer models to deliver best results is still an art and requires working with a mixture of programming, instructing, finetuning and evaluation. As a Context Engineer, you will bring discipline to this process by applying a range of techniques (such as DSPy) to systematically discover and document prompting best practices for customer-specific workloads and beyond. Collaborating closely with research engineers and platform engineers, you will design and optimize prompts that guide context layer models to produce accurate, relevant, and contextually appropriate responses. Given that context engineering is still a new domain, we suggest that you share with us any context engineering project on LLMs that you're proud of working on. Ideally, showcase how you systematically used the techniques to improve and evaluate an LLM's behavior. If you have experience with ML projects involving dataset curation and processing that will work as a substitute as well.