7 min. read

August 03, 2023

The Role of a Machine Learning Engineer

Artificial Intelligence. This buzzword has taken the software industry by storm. But what does it mean for Machine Learning engineers?


If you've paid attention to recent announcements from Google, Microsoft, or AWS, you've likely heard the term AI quite often. Today, the role of AI has changed significantly from when it was first introduced. A couple of years ago, AI was used to include supporting features in an app, today, AI is used to drive the future of software.

Look at tools like ChatGPT, Google Bard, and Dall-E. They've entirely changed the way AI is been used. These tools have focused on making AI its core and have paved the way for modern apps to take advantage of such power tools.

So, what is a Machine Learning (ML) engineer? 

What's new with AI in 2023?

Talk of the town: Generative AI & Diffusion

You've probably heard of ChatGPT, Google Bard, and Dall-E. These are some of the most popular applications that have been powered using Generative AI. If you're unfamiliar with Generative AI, just picture any form of AI capable of producing various types of content, including text, images, audio, and video. It sounds a bit creepy, doesn’t it?

Apart from Generative AI, there have been some new additions to the industry, like large diffusion models. Diffusion models became popular in recent years, they’re generative models that work by adding noise to the training data and then generating content. For example, Hugging Face has built a set of powerful diffusion models that were turned into an open-source project which developers use to build apps that generate text and images.

So, how do these transformations impact ML engineers in 2023?

1. Expanding the scope of ML applications

Building applications in 2023 requires higher levels of complexity. Clients no longer want you to build applications that can identify an object as 'cat' or 'dog'. ML engineers are no longer limited to building models that solve simple classification or regression tasks.

Out-dated ML Driven Apps

Figure: Out-dated ML Driven Apps

Today, clients want you to think big! For example, you might be asked to build a model capable of responding to customer inquiries (aka, an AI bot) or generate a banner image for a blog post. These are not simple tasks. This new requirement of broadened scope lets ML engineers think out of the box and even offers opportunities to innovate and develop something that no one has tried yet.

Figure: Modern apps powered with ML

Figure: Modern apps powered with ML

2. Data quality and diversity

With applications and ML models getting more and more complex, ML engineers need to prioritize data quality and diversity. Generative AI models rely heavily on training data to learn patterns and generate meaningful output.

Therefore, ML engineers are responsible for ensuring that the training data are diverse, representative, and free from bias. To do so, they must implement data pre-processing techniques, perform data augmentation, and validate the data. This helps optimize the performance and fairness of the model, thus, allowing them to perform well in production workloads.

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3. Ethical and responsible AI

Consider apps like ChatGPT. These applications accept user prompts and provide responses based on the given prompt. Therefore, it’s up to the ML engineer to ensure that user prompts are securely processed in order to preserve integrity and confidentiality.

While training these models, ML engineers must ensure that the model doesn’t absorb any sort of built-in prejudice and bias based on gender, race, etc. That’s why they need to implement techniques that detect and mitigate bias. They need to make sure there is transparency, and that mechanisms for user consent and data protection are in place. It helps maintain the trustworthiness and reliability of the system.

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4. Using pre-trained models with fine-tuning

OpenAI has exposed an API that ML engineers can use to fine-tune their models and build models on top of their models. Therefore, in 2023, ML engineers no longer need to go through the trouble of training their models. They can invoke an API and fine-tune a model such as the GPT-3 or GPT-3.5 for the task at hand. These models have already been trained across billions of parameters and are highly intelligent. 

5. Collaboration with Domain Experts

It's crucial that ML engineers understand the nuances of specific industries and domains to help build successful AI-powered applications. So, they must be able to communicate and collaborate closely with subject matter experts. That’s the best way to gather domain-specific knowledge that helps them define problems and ensure that their model aligns with the desired outcome.

This synergy between ML expertise and domain knowledge is essential for creating impactful AI solutions in 2023.

6. Continuous Model Monitoring and Maintenance

Applications built today have ever-changing requirements, and with AI models being used more and more, the complexity of ML solutions has increased. Today, companies often rely on third-party APIs or require some cloud-based solutions. Deploying these solutions, and monitoring and maintaining them in production is a complex task that has increased the demand for more skilled ML engineers.

For that reason, ML engineers must adopt mechanisms for model re-training, perform periodic evaluations, and implement strategies to update models with new data or adapt to changing environments. For example, you could process the user-fed data into your production application (in an ethical manner) and use these data to re-train your model and further improve your accuracy with real-time data!


It's undoubtedly an excellent time to be an ML engineer. An ML engineer can explore many avenues, from generative AI to classification, regression, clustering, and much more.

However, being an ML developer also means you must frequently watch the Generative AI space! It's certainly the talk of the town right now, and it doesn't look like anything will change anytime soon. So, I suggest you consider focusing more on learning the concepts of generative AI, prioritizing data quality, and looking into ethical considerations as data privacy is a very important aspect of modern AI applications.

The best way to Embrace learning AI and help bring about 100% autonomy across all industries is by building better AI solutions!

Thank you for reading!

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