cho.shresearchmini
| Owns model weights and behavior, not production serving |
| 5. Inference & Platform Engineer | Serving, infrastructure, SRE | Model serving, GPU infrastructure, deployment, batching, caching, scaling, latency, uptime, operational observability | Owns runtime performance, not answer quality |
| 6. AI Quality, Safety & Security Engineer | Evaluation, governance, AI security | Evaluation suites, regression tests, red teaming, guardrails, prompt-injection testing, privacy controls, release gates | Defines and verifies quality and safety requirements; other roles implement required changes |
Especially with the emergence of ChatGPT.
AI-native refers to developing and deploying artificial intelligence (AI) technologies in a way native to the cloud, leveraging the principles and practices of cloud-native development. It uses cloud computing infrastructure, services, and tools to build, train, and deploy AI models.
AI-native systems are designed to leverage cloud computing resources for data storage, processing, and analysis. They typically use cloud-based services such as data lakes, data warehouses, and analytics platforms to store and manage large volumes of data. They also use cloud-based tools such as machine learning (ML) frameworks, natural language processing (NLP) services, and computer vision APIs to develop and train AI models.
In an AI-native approach, the AI models are typically built and trained using large amounts of data from various sources. This data is often stored in the cloud and can be accessed and processed by AI models using cloud-based services and tools. In addition, AI models are often deployed in the cloud, which can be scaled up or down to handle changing workloads.
An AI-native approach's benefits include faster Time to Market, increased scalability and availability, and improved cost-effectiveness. However, it also requires specialized data science, ML, and cloud computing skills. In addition, it presents challenges around managing large volumes of data, ensuring data privacy and security, and avoiding bias in AI models.
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