Exploring the role of a machine learning engineer

Machine learning now shapes many industries, from finance to healthcare. It keeps changing how firms build campaigns, tailor services, and improve the customer experience.

At the center of this shift is the machine learning engineer. This professional blends data science and software engineering in one role.

Indeed once named it the best job in the US. Since then, demand has climbed with the rise of automation and AI tools.

Hiring a machine learning engineer in-house can be costly. So offshoring through a provider such as Outsourced can be a smart alternative. This article covers the role, its duties, and the growing demand for it.

What does a machine learning engineer do?

A machine learning engineer designs, builds, and deploys AI systems that turn data and algorithms into working real-world solutions.

In short, this is a specialized role inside the wider field of data science. It sits close to other data jobs, such as the data scientist and data analyst. Here is what the role focuses on:

• Building models and algorithms that solve business problems
• Turning those models into apps people can use
• Testing and improving systems over time

Machine learning engineers hold many duties across the machine learning development life cycle. For example, their work often includes the following:

• Verifying the quality of data
• Performing and applying statistical analysis
• Testing machine learning models
• Training and retraining AI systems
• Creating and updating machine learning libraries

Machine learning engineer vs. Software engineer

Both roles build applications. Still, they have different focuses and skill sets.

Software engineers mainly design the structure of software apps. They may add automation to their systems. However, they focus more on how the app works and feels to use.

Machine learning engineers, on the other hand, build and apply machine learning algorithms. So they make sure those algorithms work well inside the app. For larger builds, some firms combine this with software development outsourcing.

Skills required for a machine learning engineer

A machine learning engineer needs a broad mix of skills. It blends technical expertise with strong problem-solving. Here are five key skills every machine learning engineer should have.

Proficiency in programming languages

Machine learning engineers must be skilled in Python, R, Java, or C++. In fact, these languages power data processing, model building, and rollout. As a result, strong coding habits speed up every stage of a project.

Strong background in mathematics and statistics

A solid grasp of math and statistics is vital for building algorithms. Ideally, engineers know linear algebra, Bayesian statistics, and related fields well. Because of this, they can spot patterns that raw data alone would hide.

Knowledge of data preprocessing

Model quality depends on the quality of the training data. So machine learning engineers must know how to preprocess and clean data. As a result, their models stay accurate and reliable.

Familiarity with machine learning frameworks

Engineers must know popular machine learning libraries and frameworks. This helps them build models faster. For example, common tools include TensorFlow, PyTorch, and scikit-learn. Many teams pair these with wider AI and ML solutions.

Problem-solving and analytical thinking

A machine learning engineer needs sharp problem-solving skills. So they can pick the best approach for each task. In addition, they fine-tune models for the best performance. For example, they may test many settings before a model is ready to ship.

Should you hire a machine learning engineer offshore?

Demand for this role has surged in recent years. This is due to the growing use of AI and data-driven decisions. To keep up, some firms also lean on big data consulting for support.

The Future of Jobs 2023 report noted that AI and machine learning work could grow by up to 40% in the coming years. So the talent gap is real.

Hiring offshore can be the best move for firms that want a global talent pool. In addition, offshored engineers bring fresh views and ideas. As a result, they enrich the build process and help you stay competitive. Still, you should check each candidate’s skills and past projects with care.

If you are weighing an offshore hire, look no further than Outsourced, a leading provider of developers from the Philippines. They connect you with top professionals who act as an extension of your team.

Outsourced is known for sourcing the best tech staff in the Philippines, machine learning engineers included. With a large talent pool and a strict selection process, Outsourced helps you find the right fit for your business.

Frequently asked questions

What does a machine learning engineer do?

A machine learning engineer designs, builds, and deploys AI systems. So they turn data and algorithms into tools that solve real business problems.

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

A software engineer builds the structure of an app. A machine learning engineer builds and applies the algorithms inside it. In short, one focuses on the app, the other on the models.

What skills does a machine learning engineer need?

They need coding skills in Python, R, Java, or C++. In addition, they need strong math, data preprocessing, framework knowledge, and problem-solving.

Which programming languages do machine learning engineers use?

Python is the most common choice. Many also use R, Java, or C++ for data work, model building, and rollout.

Can you hire a machine learning engineer offshore?

Yes. Offshoring taps a global talent pool at a lower cost. For example, providers such as Outsourced connect firms with skilled engineers in the Philippines.