Design and implement end-to-end AI systems, integrating foundation models into various applications and workflows.
Apply and optimize model adaptation techniques, including prompt engineering, Retrieval-Augmented Generation (RAG), and finetuning, to tailor foundation models for specific use cases.
Develop and integrate AI architecture components such as context enhancement, input/output guardrails, model routers/gateways, caching mechanisms, and agent patterns to ensure system reliability and security.
Implement AI pipeline orchestration to define and chain together different components of an AI system, ensuring seamless data flow and complex workflow execution.
Optimize AI model inference for latency and cost, utilizing techniques like quantization, distillation, and parallelism, and demonstrate proficiency in working with GPUs and large compute clusters.