How to hire machine learning engineers to accelerate data and AI projects

Machine Learning has transitioned from being an experimental technology to becoming a tool used to solve specific business problems. Demand forecasting, fraud detection, recommendation systems, automatic classification, predictive maintenance, and personalization are just a few examples of applications that can be built using machine learning models.

However, there is a significant difference between developing an experimental model and getting that model to work reliably within a real product. An algorithm can achieve excellent results during testing and still be difficult to deploy, scale, monitor, or maintain in production.

This is where the role of the machine learning engineer becomes particularly relevant. This profile connects model development with software engineering, data, and the infrastructure needed to bring machine learning solutions to production environments. But when does a company really need to hire machine learning engineers? What skills should they have? And what is the difference between an ML engineer, a data scientist, and an AI engineer?

In this guide, we analyze what a company should consider before incorporating machine learning specialists into its tech team.

What does a machine learning engineer do?

A machine learning engineer is a professional specialized in developing, implementing, and maintaining systems based on machine learning models. Their work lies at the intersection of machine learning, software engineering, and data.

Depending on the project, they may participate in tasks such as:

• Data preparation.
• Feature engineering.
• Model training.
• Evaluation and validation.
• Algorithm optimization.
• Development of ML pipelines.
• Model deployment.
• Training automation.
• Production monitoring.
• Integration with applications.
• Scalability of machine learning systems.

Therefore, the profile requires more than just mathematical knowledge or experience developing models. It also needs to understand how to turn those models into technological systems that can be used stably within an organization.

When does a company need machine learning?

Not all business problems require machine learning. Before hiring specialists, it is advisable to identify whether there is truly a use case where this technology can add value. Some common scenarios include the following.

Demand forecasting: Retail, logistics, or e-commerce companies can use historical information to estimate future demand and optimize inventories.

Fraud detection: Models can identify anomalous behaviors and patterns associated with potentially fraudulent operations.

Recommendation systems: E-commerce, content platforms, and marketplaces can use machine learning to personalize recommendations based on each user's behavior.

Predictive maintenance: In industrial environments, data generated by machinery and sensors can be used to anticipate certain failures.

Classification and scoring: Machine learning can help classify large volumes of information or generate scores based on different variables.

Process automation: When there are large amounts of data and repetitive patterns, models can help automate certain decisions.

The important point is that machine learning should start with a business problem, not with an algorithm.

When to hire machine learning engineers?

A company can experiment with machine learning using data scientists or small prototypes. The ML engineer becomes particularly relevant when the project needs to evolve into production.

You have models that still function as prototypes

A common situation is having models developed in notebooks that provide good results but are not yet integrated with the company's systems. The machine learning engineer helps turn those experiments into production-ready services.

You need to integrate models within an application

A prediction only generates value when it can be used within a real process.

Building that integration requires engineering knowledge in addition to machine learning.

You need to automate training and deployment

When models are constantly evolving, manually performing the entire process becomes inefficient. This is where MLOps practices come into play.

The volume of data or predictions is growing

A model that works correctly with a few users may encounter problems when it needs to process millions of requests. Scalability then becomes an engineering requirement.

You need to monitor models in production

Models can lose accuracy over time if data or user behavior changes. Therefore, mature organizations continuously monitor their performance.

What skills should a machine learning engineer have?

The stack will depend on the project, but there are certain capabilities that are especially relevant.

Python

Python remains one of the predominant languages in machine learning. Its ecosystem allows for data processing, model training, experimentation, and deployment.

Scikit-learn

Scikit-learn is widely used for classic machine learning problems such as classification, regression, clustering, dimensionality reduction, and preprocessing.

It is especially useful for developing models on structured data.

TensorFlow

TensorFlow allows for the development and deployment of machine learning and deep learning models. It can be used in projects related to neural networks, computer vision, natural language processing, and other advanced systems.

PyTorch

PyTorch is another of the most relevant frameworks in the machine learning and deep learning ecosystem. It is widely used in both research and enterprise applications.

The choice between TensorFlow and PyTorch will depend on the project context and the existing technological ecosystem.

MLOps: bringing machine learning to production

This section is particularly important because it differentiates this article from a generic one on data science. MLOps applies automation and operational principles to the machine learning lifecycle. The goal is to ensure that models can be developed, deployed, monitored, and updated in a controlled manner.

Tools like MLflow, Databricks, and various cloud services help manage these processes. When an organization transitions from experimenting with models to relying on them for critical processes, MLOps becomes significantly important.

Machine learning on AWS, Azure, and Google Cloud

The major cloud providers offer services to develop and operate machine learning projects. This allows for the combination of storage, data processing, model training, and infrastructure within the same ecosystem. An ML engineer can work with platforms such as:

AWS, Microsoft Azure, or Google Cloud, depending on the organization's architecture.

For certain projects, cloud experience is particularly important because training and running models may require significant computational resources. However, we must again avoid hiring based solely on a simple list of tools. The professional must understand the principles of machine learning and architecture that exist behind them.

Machine learning engineer vs data scientist

This is probably one of the most important questions for a tech leader.

Both profiles work with models, but their focus tends to be different.

The boundary is not always absolute.

In small companies, the same professional may assume part of both functions.

But as technological maturity increases, specialization tends to be greater.

Data engineer vs machine learning engineer

There is also a significant difference with data engineering. The data engineer builds the infrastructure that collects, processes, and provides data. The machine learning engineer uses that data to build and operate machine learning systems. We can represent it simply:

Both profiles often work closely together. In fact, poor data infrastructure can become one of the biggest obstacles to any machine learning project.

Machine learning engineer vs AI engineer

With the growth of generative artificial intelligence, another profile has emerged: the AI engineer. Although there is overlap, the focus may be different.

A machine learning engineer typically works on training, optimizing, and operating predictive models. An AI engineer may focus more on building solutions using existing artificial intelligence models, model APIs, LLMs, RAG systems, agents, and applications based on generative AI.

For example:

Machine learning: predicting which customers are most likely to churn from a service.

Generative AI: building an assistant that queries business documentation and answers questions.

This differentiation will allow us to correctly separate the machine learning and artificial intelligence landings of lateam.

What seniority does your project need?

Not all projects require the same profile.

Machine learning engineer: Can work on previously defined models and architectures, develop pipelines, and integrate existing solutions.

Senior machine learning engineer: Can make complex technical decisions related to architecture, optimization, scalability, and production.

ML tech lead: Coordinates machine learning teams, defines standards, and makes decisions related to architecture and tools.

ML architect: Designs complete architectures for large-scale machine learning systems.

The choice should depend on the maturity of the project and the autonomy needed by the professional.

Should you hire internally or outsource machine learning engineers?

Machine learning is precisely one of the areas where it can be challenging to maintain all the necessary specializations internally.

A project may require:

• Data engineering.
• Data science.
• Machine learning.
• Cloud.
• MLOps.
• Backend.
• DevOps.

If the organization already has a data team, it can incorporate one or more specialists through IT outsourcing to cover specific capabilities. When the need is broader, a team-as-a-service model allows for building a multidisciplinary team.

For example:

Data engineer + data scientist + ML engineer + cloud engineer + backend developer.

The composition can be adapted as the project transitions from experimentation to production.

How lateam incorporates machine learning specialists

At lateam, we help companies incorporate machine learning engineers and other specialists in data and artificial intelligence through IT outsourcing and team-as-a-service models. Before starting the selection process, we analyze the problem the organization wants to solve, the available data infrastructure, the technological stack, and the project's maturity level.

From there, we can identify profiles with experience in technologies such as Python, Scikit-learn, TensorFlow, PyTorch, MLflow, Databricks, and cloud platforms. We also value experience working with international teams, agile methodologies, and the ability to collaborate with data engineers, data scientists, developers, and product managers.

The goal is not simply to find someone who can train a model. It is to incorporate professionals capable of turning machine learning into a solution that works within the business.

Frequently asked questions about machine learning engineers

What does a machine learning engineer do?

They develop, implement, and maintain systems based on machine learning. They may participate in data preparation, training, validation, deployment, monitoring, and optimization of models.

What technologies does a machine learning engineer use?

Python is one of the most common technologies, along with frameworks and tools like Scikit-learn, TensorFlow, PyTorch, MLflow, and cloud platforms.

What is the difference between a data scientist and a machine learning engineer?

The data scientist typically focuses on analysis, experimentation, and model development, while the machine learning engineer places greater emphasis on turning those models into production-ready systems.

What is the difference between a data engineer and a machine learning engineer?

The data engineer builds the infrastructure that allows for data collection and preparation. The machine learning engineer uses that information to develop and operate machine learning systems.

What is MLOps?

MLOps encompasses practices and tools aimed at automating and managing the lifecycle of machine learning models, from their training and validation to their deployment and monitoring.

When should I hire machine learning engineers?

When a company needs to bring models to production, automate ML processes, integrate predictions into applications, or build scalable machine learning systems.

Can machine learning engineers be outsourced?

Yes. They can be incorporated individually through IT outsourcing or be part of specialized teams through a team-as-a-service model.

Conclusion

Implementing machine learning is not just about training an algorithm.

The real challenge begins when that model needs to connect with real data, integrate into an application, respond to thousands of users, and maintain its performance over time.

This is where machine learning engineers provide especially valuable capability.

They combine knowledge of machine learning with software engineering, infrastructure, and automation to transform experiments into systems capable of functioning in production. Therefore, before starting a hiring process, a company should ask itself where its project truly stands. Does it need to analyze data? Build infrastructure? Experiment with models? Or bring those models to production?

Correctly identifying that need allows for determining whether the appropriate profile is a data engineer, data scientist, machine learning engineer, or AI engineer.

Incorporate machine learning specialists into your team

At lateam, we help companies incorporate machine learning engineers and other specialists in data and artificial intelligence through flexible IT outsourcing and team-as-a-service models. Whether you need a specialist to strengthen your team or to build a complete machine learning capability, we can help you identify the right profiles for your project.