AI Architecture and Design: Design and develop advanced AI models and frameworks (including large language models, transformers, and agent-based systems), aligning AI initiatives with business goals. Provide technical leadership to data science and engineering teams, and set strategic direction for AI projects.
Architectural Design: Design and oversee the integration of AI components such as LLMs, transformer architectures, retrieval-augmented generation (RAG) workflows, and vector databases into scalable solutions. Establish standards for model architecture and pipeline optimization to ensure robustness and efficiency.
Evaluation & Safety: Define and enforce rigorous evaluation protocols for AI models, including performance metrics, validation techniques, and safety checks. Ensure all AI systems meet reliability standards, ethical guidelines, and regulatory requirements for responsible AI usage.
MLOps & Deployment: Guide the development of MLOps pipelines for continuous training, testing, and deployment of models in cloud environments. Oversee infrastructure decisions (without bias to specific platforms) to guarantee that AI services are scalable, secure, and maintainable in production.
