What’s a Gen AI Engineer? How Is It Different from an SDE Role? Expectations in AI Engineer Interviews & Resources for Aspiring Candidates

Artificial Intelligence (AI) is taking over the tech world. If you’ve been paying attention to industry trends, you've likely seen the rise of Generative AI (Gen AI), an area that's revolutionizing everything from content creation to complex problem-solving. But what does it mean to be a Gen AI Engineer? How does this role differ from the traditional Software Development Engineer (SDE) role, and what are interview expectations for AI engineers? Let’s dive in and unpack all the nuances, plus I’ll share some resources to help you navigate your journey in this space.

1. What Exactly is a Gen AI Engineer?

Before we get into the specifics of how a Gen AI Engineer differs from an SDE, let’s break down what a Gen AI Engineer actually does. Think of them as the architects behind the magic of AI models that can generate content—whether that’s text, images, music, or even code. These engineers create and fine-tune machine learning models, working specifically with models that generate outputs based on input data.

They are specialists in:

• Training and fine-tuning models: You’ll be handling models like GPT, BERT, or Stable Diffusion to generate text, code, or other outputs.
• Implementing Generative Algorithms: You’ll be writing and optimizing algorithms that allow AI to produce realistic, human-like outputs.
• Data handling: Gen AI engineers manage massive datasets to ensure their models are learning from high-quality, diverse, and well-labeled data.
• Model deployment: It’s not just about building the models but getting them out into the real world, serving millions of queries a day, while ensuring they remain scalable and performant.

To make it simple, if an SDE builds the structure of an app, the Gen AI engineer builds the “mind” behind it. They're responsible for teaching machines to think and generate content autonomously, often at a creative level.

2. Gen AI Engineer vs. Software Development Engineer (SDE)

Now, let’s get to the crux of the matter: how is a Gen AI Engineer’s role different from an SDE?

Core Focus

• Gen AI Engineer: The focus is AI-centric—building systems that can think, generate, and predict. You work with ML models, neural networks, and data pipelines.
• SDE: The focus is software-centric—you design, develop, and maintain software systems, often involving more traditional programming languages like Java, Python, and C++.

Technical Skills

• Gen AI Engineer: Strong expertise in AI/ML, deep learning frameworks (TensorFlow, PyTorch), and generative models. A deep understanding of algorithms, probability, and statistics is essential. It’s not just about coding—it's about training and optimizing models that can handle massive datasets.
• SDE: Primarily focuses on algorithms, data structures, and building applications. You’re still writing code, but your work often revolves around ensuring that code functions within an application, not creating systems that generate new content autonomously.

Tools and Technologies

• Gen AI Engineer: Works with frameworks like TensorFlow, PyTorch, and often cloud platforms like AWS, GCP, or Azure for deploying AI models.
• SDE: Works with standard software development tools (IDEs, version control systems) and backend technologies like Java, C++, and Node.js. You might occasionally dabble in machine learning, but it’s not the focus.

End Goal

• Gen AI Engineer: The ultimate goal is to empower AI systems to generate creative, useful outputs from large datasets (e.g., creating new art, writing, code).
• SDE: The ultimate goal is to build applications that are reliable, fast, and scalable.

So, in short, a Gen AI Engineer is deeply involved in the intersection of AI theory and practical application in the creative space, while an SDE is focused on software systems, frameworks, and application design.

3. What’s Expected in Gen AI Engineer Interviews?

If you’re thinking about pivoting to the role of a Gen AI Engineer, here’s what you can expect in the interview process:

A) Technical Depth

Expect to go deep into machine learning, especially deep learning, generative models, and AI ethics. You’ll be asked to demonstrate a deep understanding of:

• Neural Networks: Types (CNNs, RNNs, GANs) and their applications.
• Optimization: How to optimize large models for accuracy and efficiency.
• Data Handling: How to clean, preprocess, and augment data for training.
• Performance Metrics: Understand metrics like accuracy, precision, and recall, especially in the context of generative models.
• Model Deployment: Experience with deploying models to production, ensuring scalability, and managing resource-intensive processes.

B) Coding Challenges (But with a Twist)

As a Gen AI Engineer, you will still face coding challenges, but these will be oriented around building AI models. Expect questions like:

• Implement a simple neural network from scratch (without using high-level frameworks).
• Optimize a given model for faster inference.
• Apply data augmentation to a dataset and build a generative model on it.

Expect Python to be your language of choice in these coding challenges, as it’s the lingua franca for AI development.

C) System Design Interviews (AI Style)

Yes, you’ll be asked to design systems—but these won’t be just about scaling a web app. Instead, you’ll be designing:

• AI-powered systems: How to design an AI model that can generate coherent text (e.g., GPT) or realistic images (e.g., GANs).
• Data pipelines: How to build a pipeline that can handle streaming data for real-time model predictions.

It’s not just about scaling software systems—it’s about scaling AI systems for optimal performance and generating accurate outputs at scale.

D) Problem-Solving with AI

Expect to solve complex AI-related problems that require both critical thinking and a strong understanding of AI algorithms. You’ll be given a problem statement, and you may be asked to train a model to solve it, or optimize an existing model for better performance.

4. Resources to Prepare for a Gen AI Engineer Role

If you’re aiming to become a Gen AI Engineer, here are some resources that will set you up for success:

A) Books

1. “Deep Learning” by Ian Goodfellow: The definitive book on deep learning, covering everything from the fundamentals to advanced techniques.
2. “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron: Practical guide with real-world examples of machine learning and deep learning.

B) Online Courses

1. Andrew Ng’s Machine Learning Course (Coursera): A must-do for anyone stepping into AI. It provides the foundation of machine learning, with clear explanations and hands-on exercises.
2. Stanford’s CS231n: Convolutional Neural Networks for Visual Recognition: The gold standard if you’re serious about learning deep learning and image recognition.
3. Fast.ai’s Practical Deep Learning for Coders: An amazing course to get into deep learning quickly using the Fast.ai library.

C) YouTube Channels

1. Lex Fridman Podcast: Interviews with top researchers and practitioners in AI and machine learning.
2. Two Minute Papers: Breaks down the latest AI research papers in an easy-to-understand format.

D) Practice Platforms

1. Kaggle: A platform where you can practice real-world machine learning problems and compete in challenges.
2. LeetCode (AI/ML Challenges): For coding and algorithm practice specific to AI-related topics.

5. Popular Gen AI Certifications to Consider

If you're aiming to upskill in this space, consider these certifications as they offer practical, industry-relevant knowledge and training:

1. DeepLearning.AI - Generative Adversarial Networks (GANs) Specialization (Coursera) This course, offered by DeepLearning.AI on Coursera, is a great place to start if you want to dive into generative models. It covers GANs, a popular technique for generating new content, and will give you a strong foundation in understanding how to build and train AI models to generate new content.
2. AWS Certified Machine Learning - Specialty Amazon’s AWS certification program for machine learning is an excellent option for those looking to strengthen their expertise in the cloud while focusing on machine learning. It covers many of the tools used for building and deploying generative AI models.
3. Google Cloud Professional Machine Learning Engineer Certification Google’s certification helps you hone your machine learning and AI skills, including those required for working with generative models. It’s a bit more expansive than other AI certifications, covering a broader spectrum of machine learning and AI engineering tasks.
4. Microsoft Certified: Azure AI Engineer Associate If you’re working in Microsoft’s ecosystem, the Azure AI Engineer certification is a good option. It will help you develop AI models, including generative models, on Microsoft’s cloud platform.

6. Final Thoughts & Call to Action

Becoming a Gen AI Engineer is an exciting and highly rewarding path, but it requires a different mindset and technical expertise than a traditional software development role. The world is changing, and AI is at the center of that change.

Do you have the skills to push the boundaries of what AI can do? If you’re passionate about generative models, deep learning, and creative AI applications, now is the time to upskill and dive into this field.

Are you ready to take the plunge into the world of Generative AI? Let’s chat in the comments!