The evolution of AI development, particularly with the advent of Large Language Models (LLMs) like GPT-4 and open-source alternatives such as LLaMa, has transformed the requirements for AI teams, reducing the need for specialized machine learning engineers and opening up AI adoption to more companies. Prompt engineering has emerged as a key skill, emphasizing the creation of natural language instructions that leverage the pre-trained capabilities of LLMs, and does not necessitate deep technical or mathematical expertise. This allows domain experts and product managers to play a more direct role in AI product development, thereby reducing engineering time and enhancing the feedback loop. The role of the 'AI Engineer' has become central, bridging technical and non-technical teams and focusing on integrating AI into traditional software systems through full-stack engineering and techniques like fine-tuning and retrieval augmented generation. This shift in roles and skills has led to more collaborative and dynamic AI teams, where product managers and domain experts engage closely with engineers to shape AI-driven experiences.