Applied AI Software Engineer

About Cookpad
Cookpad is a food tech company dedicated to shaping the future of home cooking and food as a pillar for a sustainable future. At our Bristol headquarters we have been developing innovative product ideas and we have a new product launch in 5 countries: Japan, Indonesia, UK, France and Italy.

Moment helps people learn to cook in an innovative way with a personal coaching service. This service is completely based in AI using multimodal (text, vision, audio).

About the role
Joining Cookpad as an Applied AI Engineer you will be asked to solve interesting problems associated with turning machine learning models into a service. This will include building functionalities that need to be implemented in order to ensure fully production-ready code. 

You will communicate ideas and demos to team members and take ownership of how a feature is productionised, built and deployed.

You will be highly proficient in coding in Python and have proven hands-on experience in building applications and integrations of modern generative AI models.

It’s not for everyone. Because of our speed and growth, it can feel chaotic. We work in a small, collaborative team and in a creative, fast-paced environment. Initially our focus is small scale production. 

The role is based in Bristol on a hybrid working arrangement and we are happy to hear from applicants seeking either full-time contract or permanent employment.

Key Responsibilities:

Leverage cutting-edge AI technologies to address real-world customer challenges. Collaborate with product team to translate business and product requirements into technical solutions that leverage the latest in AI technologies. Design and implement intelligent AI agents and multi-agent systems capable of autonomous decision-making and interaction, enabling complex workflows and real-time problem-solving. Develop agent-based architectures for applications such as task orchestration, adaptive learning, and dynamic data analysis. Create and deploy Retrieval-Augmented Generation (RAG) systems to enhance AI model retrieval and contextual response accuracy. Evaluate and refine prompt engineering strategies, incorporating advanced techniques to optimize generative model outputs for diverse use cases.