Own the architecture and the deployment. Define how our generative AI systems are structured and integrate with our computer vision stack, and take models from experiment to production.
Define and implement the "Definition of Done" for AI using quantitative evaluation. Use evaluation tools (e.g. DeepEval) to score models on Faithfulness and Relevancy. Use these metrics to validate model upgrades and cost-optimisation experiments
Conduct research and stay up to date with the latest advancements in AI and machine learning, integrating new technologies and methodologies into the team’s workflows.
Develop the foundational platform for AI Agents - including agent registry, evaluation framework, prompt management, vector stores, memory layers, and tool integrations
Provide technical and product leadership across the end-to-end AI lifecycle, from research, experimentation and prototyping through production deployment, monitoring and continuous improvement.