AI Engineers- What Do They Do?

Summary

In this video, Krish Naak explores the multifaceted role of an AI Engineer, a position that has been integral to the data analytics industry for over half a decade. Drawing from his experience at companies like Panasonic, Krish explains how AI Engineers often overlap with roles such as data scientists and machine learning engineers, emphasizing the need for versatility in startups. He outlines the responsibilities of designing, building, and deploying AI platforms, integrating models into applications, and handling retraining with new data. The video promises a roadmap for aspiring AI Engineers and insights into resume building, aiming to clarify the dynamic ecosystem of AI roles.

Takeaways

• 👋 Introduction to AI Engineers: AI Engineer roles have been prevalent in the data analytics industry for the past 6 to 7 years.
• 📊 Role Overlap: In many companies, especially startups, roles like AI Engineers, Data Scientists, Data Analysts, and Machine Learning Engineers often overlap.
• 🧑‍💼 Specificity in Larger Companies: Research and product-building oriented companies tend to have more specific roles for AI Engineers compared to startups.
• 🎥 Video Series Plan: The video is part of a series covering AI Engineers, including their roles, a roadmap for aspiring AI Engineers, and resume discussions.
• 💼 Job Descriptions: Reviewing job descriptions for AI Engineers to understand their specific responsibilities and how they differ from roles like Machine Learning Engineers and Data Scientists.
• 🚀 Responsibilities: AI Engineers help design, build, and deliver scalable machine learning and AI platforms, integrate models into various applications, and ensure models are accessible at scale.
• 🔄 Model Lifecycle: AI Engineers are involved in retraining models with new data, ensuring seamless integration, and maintaining the scalability and reliability of AI systems.
• 🧩 Generative AI: With the rise of generative AI, AI Engineers need to integrate pre-trained foundation models and ensure seamless deployment and fine-tuning.
• 🛠️ Technology Stack: Experience with AI/ML orchestration and serving software, as well as cloud technologies like AWS, Azure, or Google Cloud, is crucial for AI Engineers.
• 📱 Practical Example: The speaker shares a personal example from Panasonic, where they worked as a data scientist, ML engineer, and AI engineer on an IoT project involving smart air conditioners.

Q & A

What is the main role of an AI Engineer in the data analytics industry?

The main role of an AI Engineer in the data analytics industry is to design, build, and deploy AI and machine learning models, ensuring they are scalable and integrated with various platforms like mobile apps, web applications, and edge devices.

How do the roles of AI Engineers, Data Scientists, and Machine Learning Engineers overlap in startups?

In startups, the roles of AI Engineers, Data Scientists, and Machine Learning Engineers often overlap, requiring individuals to handle tasks across these domains, such as data analysis, model training, deployment, and integration with applications.

Why is the role of an AI Engineer considered crucial in research-oriented and product-building companies?

In research-oriented and product-building companies, the role of an AI Engineer is crucial because they focus on developing and deploying AI solutions that are essential for the company's innovation and product development, ensuring that AI models are seamlessly integrated into the end products.

What tasks does an AI Engineer handle post model creation?

Post model creation, an AI Engineer handles tasks such as deploying the model in a scalable manner, ensuring it can handle multiple requests, integrating the model with various applications, and maintaining and fine-tuning the model with new data.

How does an AI Engineer contribute to the deployment and integration of AI models?

An AI Engineer contributes to the deployment and integration of AI models by setting up CI/CD pipelines, using technologies like Docker and Kubernetes for scalability, and integrating the deployed models with applications through APIs.

What is the significance of fine-tuning in the role of an AI Engineer?

Fine-tuning is significant for AI Engineers as it involves adjusting pre-trained models with new data to improve their performance for specific tasks, ensuring that the AI solutions remain effective and up-to-date.

How has the emergence of generative AI influenced the role of AI Engineers?

The emergence of generative AI has influenced AI Engineers to work with generative AI frameworks, integrate foundation models with applications, and ensure that these models are fine-tuned and deployed effectively for various enterprise applications.

What is the role of AI Engineers in managing scalability and robustness of AI applications?

AI Engineers manage the scalability and robustness of AI applications by designing and implementing solutions that can handle high volumes of requests, ensuring continuous availability, and integrating AI models into the overall application architecture.

Can you explain the integration process of AI models with end products as performed by AI Engineers?

AI Engineers integrate AI models with end products by deploying the models on cloud platforms, creating APIs for interaction, and embedding these APIs into applications, ensuring seamless functionality and user experience.

What example does Krish Naak provide from his experience to explain the role of an AI Engineer?

Krish Naak provides the example of his work at Panasonic, where he was involved in making air conditioners smart by integrating AI models with IoT devices, cloud services, and mobile applications, thereby showcasing the multifaceted role of an AI Engineer in developing and deploying AI solutions.