Reasons You Should Consider Hiring a Machine Learning Engineer in 2025

1. Automating repetitive business processes

Tired of spending valuable time on manual, repetitive tasks? Machine learning engineers develop AI models that handle these processes for you, boosting efficiency and reducing costs.

AI-powered chatbots, for example, can take over customer service inquiries, providing instant responses and reducing human involvement in routine queries. This is a measurable advantage, as businesses using AI-powered chatbots have seen a 30% reduction in handling time, allowing teams to focus on higher-value work.

Beyond customer service, ML automation enhances everything from data entry to supply chain management, helping businesses operate smarter and faster.

2. Gaining competitive insights from data

Data is only useful if you know what to do with it. Machine learning engineers create AI-driven models that transform raw data into valuable business insights.

Retailers use ML to predict shopping trends, finance companies rely on it for risk assessment, and marketing teams optimize campaigns based on real-time analytics. It’s no surprise that 79% of corporate strategists see AI-powered analytics as critical to their success.

Companies investing in machine learning gain deeper customer insights, better demand forecasting, and more precise decision-making—all of which lead to a competitive advantage.

3. Enhancing personalization for customers

Consumers expect personalized experiences, and ML engineers make this possible. From recommendation engines like those used by Netflix to customized shopping suggestions, machine learning drives better engagement.

Studies show that “71% of consumers expected companies to deliver personalized interactions,” meaning ML will help your business improve its customer experience. 

ML engineers build models that analyze customer behavior, predict preferences, and tailor experiences automatically, all of which lead to higher conversions and improved customer retention.

For businesses, this means stronger brand loyalty, increased sales, and a better overall user experience without the need for constant manual adjustments.

4. Strengthening cybersecurity and fraud detection

AI and cybersecurity can be a double-edged sword. Because of AI, cyber threats are becoming increasingly sophisticated, and manual security measures simply can’t keep up. Machine learning engineers develop AI models that detect anomalies in real time, flagging suspicious activity before it becomes a major threat.

Financial institutions, for example, use ML-powered fraud detection to identify unusual spending patterns and stop fraudulent transactions before they happen. With cyberattacks growing in complexity, businesses that fail to integrate AI-driven security measures put themselves at serious risk.

5. Supporting real-time decision making

In fast-moving industries, timing is everything. AI and ML engineers develop models that analyze data instantly, providing businesses with actionable insights when they need them most.

For example, AI-powered trading systems in finance execute trades in milliseconds based on shifting market conditions, while real-time inventory management helps retailers prevent stockouts or overstocking. This ability to make faster, more informed decisions is what sets AI-driven businesses apart from their competitors.

With the demand for real-time analytics growing, hiring an ML engineer means your business is ready to make data-driven decisions on the fly.

1. Cutting costs without sacrificing quality

A US-based ML engineer can cost well over six figures annually—a price tag many businesses can’t afford. Meanwhile, hiring offshore in Colombia, Argentina, Poland, India, or the Philippines offers the same level of expertise at significantly lower rates.

For example, the average annual salary of an ML Engineer in Latin America ranges from $45K to $100K. In contrast, in the US, the numbers range from $146K to $191K.

Cost savings don’t mean cutting corners, either. Many offshore ML engineers have advanced degrees, work with top global tech firms, and deliver high-quality AI solutions.

2. Access to a larger talent pool

The US is experiencing a shortage of ML engineers, making it tough and expensive to hire the right candidate. Expanding your search globally gives businesses access to highly skilled professionals from regions where AI expertise is booming, such as Latin America, Eastern Europe, and Southeast Asia.

What is offshore vs. nearshore hiring?

Offshore hiring means recruiting talent from distant countries like India and the Philippines for significant cost savings. The trade-offs mainly include substantial time zone differences that may require some flexibility in collaboration.

Nearshore hiring is a type of offshoring but from countries in similar time zones. For US businesses, this means countries in Latin America (LatAm), including Mexico, Brazil, and Colombia. Hiring ML engineers from LatAm means you still get major cost savings, but with real-time collaboration, thanks to overlapping work hours.

Using a dedicated ML and AI staffing agency streamlines the hiring process, as they handle candidate sourcing, vetting, interviews, and payroll, making sure that you get the right fit.