What is the main difference between Cloud Engineers and AI/ML Engineers?

Cloud Engineers focus on designing and managing cloud infrastructure, ensuring efficient data storage and computing capabilities. AI/ML Engineers, however, create algorithms that allow machines to learn from data, enabling artificial intelligence applications. While both roles are technically demanding, they cater to different aspects of technology.

Which role offers a higher salary?

On average, AI/ML Engineers earn slightly more than Cloud Engineers. In 2025, AI/ML Engineers average around $135,000 annually, while Cloud Engineers average about $130,000. However, salaries can vary based on experience, location, and the specific industry.

What skills are essential for success in these roles?

Cloud Engineers need expertise in cloud platforms (AWS, Azure, Google Cloud), as well as networking, security, and system architecture. AI/ML Engineers require strong programming skills (particularly in Python or R), statistical analysis, and knowledge of machine learning frameworks and algorithms.

What are the career growth opportunities for Cloud Engineers?

Cloud Engineers can advance to roles like Senior Cloud Engineer, Cloud Architect, or Cloud Consultant. They may also explore positions in related areas such as DevOps, IT security, or system administration, allowing for a diverse career trajectory.

What kind of benefits do these positions typically offer?

Both Cloud Engineers and AI/ML Engineers typically receive benefits like health insurance, retirement plans, and paid time off. AI/ML Engineers may have access to additional perks like bonuses and stock options due to the competitive demand for their skill sets.

Is it easier to transition between these two roles?

While the roles are distinct, there is some overlap in skills, especially in programming and data management. A Cloud Engineer may transition to AI/ML with additional training in machine learning concepts and algorithms. Similarly, AI/ML Engineers can pivot to cloud-related roles with experience in cloud infrastructure.

How does location affect salaries in these fields?

Salaries for both Cloud Engineers and AI/ML Engineers can vary significantly based on geographic location. Urban areas and tech hubs like Silicon Valley or New York City typically offer higher salaries due to the cost of living and greater demand for tech talent.

Complete Cloud Engineer vs AI/ML Engineer Salary Comparison (2026)

Quick Comparison

Cloud Engineer

$129,167

avg. annual salary

Ai/ml Engineer
pays more on average

Ai/ml Engineer

$138,939

avg. annual salary

As technology evolves, so do career opportunities within the IT sector. One of the most intriguing comparisons is between Cloud Engineers and AI/ML Engineers. Both roles demand specialized skills, yet they cater to distinct aspects of technology. Cloud Engineers focus on managing and optimizing cloud infrastructure, while AI/ML Engineers develop algorithms and models that enable machines to learn and make decisions. With the growing demand for cloud solutions and the rising interest in artificial intelligence, understanding the salary landscape for both roles can help aspiring professionals make informed career choices. In this guide, we will dive into the average salary, benefits, and career paths for both Cloud Engineers and AI/ML Engineers to give you a comprehensive view of what to expect in 2025.

Salary by Experience Level

Cloud Engineer Entry

$95,000

starting salary

Ai/ml Engineer Entry

$102,188

starting salary

Salary Difference

$9,773

avg. difference (8%)

Average Salary Overview

In 2025, the average salary for Cloud Engineers stands at approximately $130,000 annually, with entry-level positions starting around $95,000 and senior roles reaching about $160,000. Conversely, AI/ML Engineers command slightly higher salaries, averaging around $135,000.

Entry-level positions in AI/ML start near $100,000, while experienced engineers can earn upwards of $175,000. Factors influencing these salaries include location, industry, and level of expertise.

Benefits Comparison

Both Cloud Engineers and AI/ML Engineers enjoy robust benefits packages. Common benefits include health insurance, retirement plans, and paid time off.

However, AI/ML Engineers may have an edge with more opportunities for bonuses, stock options, and professional development programs due to the competitive nature of the field. Companies in tech hubs are also more likely to provide additional perks such as flexible work arrangements and continuous learning resources.

Career Path Prospects

The career paths for Cloud Engineers and AI/ML Engineers differ significantly. Cloud Engineers can progress to senior roles, cloud architecture, or cloud consultancy.

They have opportunities to switch to related sectors like cybersecurity or DevOps. On the other hand, AI/ML Engineers can transition to data science or machine learning management roles, potentially moving into research and development positions.

With the rapid advancements in AI technologies, the demand for skilled professionals in this area is projected to grow substantially.

Skills Required

Cloud Engineers should be proficient in cloud platforms like AWS, Azure, and Google Cloud, along with networking and security. In contrast, AI/ML Engineers need a strong background in programming languages such as Python or R, statistical analysis, and proficiency in machine learning frameworks.

Continuous learning through certifications and courses is crucial for both fields to keep up with technological advancements.

Detailed Comparison: Cloud Engineer vs AI/ML Engineer (2025)

Cloud Engineer and AI/ML Engineer roles overlap but pay differently depending on skills, location, and industry. In the U.

•Cloud Engineer: entry $80k–$110k, mid $110k–$150k, senior $150k–$210k.
•AI/ML Engineer: entry $95k–$130k, mid $130k–$185k, senior $185k–$260k.

On average, AI/ML Engineers earn about 15–25% more than Cloud Engineers because model expertise commands premium rates. However, specific skills shift pay quickly.

•An AI/ML Engineer with production LLM experience can add $20k–$40k to base pay.
•A Cloud Architect with multi-cloud and Kubernetes production experience typically adds $15k–$30k.

Industry and location matter: finance and biotech roles often pay 10–30% above tech average; San Francisco and New York premiums range 15–35% versus midwest markets. Certifications move the needle modestly: AWS Professional certs or Google Professional Data Engineer average +$7k–$12k in salary.

Actionable takeaways:

•If you want higher starting pay, focus on applied ML (LLMs, production pipelines).
•If you prefer stability and broader roles, pursue cloud architecture + multi-cloud skills.
•Negotiate using industry premiums (10–30%) and list production impact (latency, cost savings, model AUC) as evidence.

Frequently Asked Questions

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