Responsibilities:

• 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.
• C...