Want to land a six-figure cloud job but not sure which role to pursue?
With so many high-paying opportunities in the cloud space, choosing the right path can feel overwhelming. Should you aim to become a cloud engineer? A cloud architect? Maybe DevOps is more your style. Or is cloud AI/ML engineering – or AI/ML architecture – the smartest path in today’s AI-driven market?
In this guide, we’ll compare these top cloud computing careers, break down the key differences, outline the core skills you’ll need for each, highlight current salary trends, and help you understand where the strongest opportunities are right now.
Whether you’re just starting out or looking to level up, this will help you choose the right path to build a career in cloud computing.
Understanding the top cloud roles
Let’s begin by clarifying what each of these roles involves in the real world.
Cloud engineer
Cloud engineers are responsible for building and managing cloud infrastructure. They work directly with services like EC2, S3, IAM, VPC, and RDS. They deploy workloads, maintain infrastructure, and automate processes using infrastructure as code tools like Terraform and AWS CloudFormation.
This role requires a strong technical foundation. If you like getting hands-on, writing scripts, troubleshooting performance issues, and optimizing cloud environments, this could be the right fit for you.
Cloud architect
Cloud architects take a more strategic approach. They design high-level solutions that meet business goals around scalability, performance, and cost-efficiency.
Architects focus on system design, stakeholder communication, and big-picture planning. It’s less about building systems yourself and more about designing the blueprint others will follow.
If you enjoy solving complex problems, communicating with leadership teams, and making technology decisions that align with business priorities, this is a strong choice.
DevOps engineer
DevOps engineers bridge the gap between development and operations. Their job is to automate software delivery, streamline infrastructure management, and improve release speed and reliability.
They work with CI/CD tools like GitHub Actions or Jenkins, manage containers with Docker and Kubernetes, and write scripts to improve automation. If you’re passionate about pipelines, automation, and reducing deployment headaches, DevOps is a rewarding path.
Cloud AI/ML engineer
Cloud AI engineers work at the intersection of cloud and artificial intelligence. They build and deploy machine learning models using services like Amazon SageMaker and Amazon Bedrock.
They collaborate with data scientists, software developers, and business stakeholders to bring AI-powered applications to life. Think fraud detection, recommendation systems, chatbots, and real-time analytics.
This is one of the fastest-growing roles in tech, as demand surges for professionals who can bring AI into production using scalable cloud infrastructure.
AI/ML Architect
AI/ML Architects sit at the strategic level of machine learning implementation in the cloud. While Cloud AI/ML Engineers focus on building and deploying models, AI/ML Architects are AI/ML Architects operate at a strategic level, designing scalable and secure ML systems that integrate deeply with cloud infrastructure.
They define architecture for model training and inference pipelines, advise on model selection, and oversee end-to-end AI implementations.
This role is ideal for professionals with experience in both AI and cloud who want to lead technical direction and system design.
Skills required for each role
Each path requires a different set of technical and soft skills. Here’s a quick breakdown:
Cloud engineer
- AWS/GCP/Azure core services
- Infrastructure as code (Terraform, CloudFormation)
- Scripting (Python, Bash)
- Networking, security, automation
Cloud architect
- Deep understanding of cloud services and architecture best practices
- Stakeholder communication and solution design
- Cost optimization and security planning
- Broad knowledge across multiple services and architectures
DevOps engineer
- CI/CD tools (GitHub Actions, Jenkins, AWS CodePipeline)
- Containers and orchestration (Docker, Kubernetes)
- Monitoring and observability tools
- Strong scripting and automation mindset
Cloud AI/ML engineer
- Python and ML libraries (Scikit-learn, TensorFlow, PyTorch)
- AWS AI services (SageMaker, Bedrock, Rekognition, Comprehend)
- Data preprocessing and MLOps
- AI model deployment and scaling on cloud platforms
AI/ML Architect
- Advanced cloud architecture experience
- Strong machine learning background (TensorFlow, PyTorch)
- MLOps and scalable model deployment strategies
- Experience designing complex data pipelines
- Collaboration with cross-functional teams and stakeholders
Click the image above to learn more about the Highest Paying Cloud Jobs in 2025 from our youtube channel
Which cloud job pays the most?
Here’s a comparison of average salaries in the United States based on recent data:
- DevOps engineer – $127,000+
- Cloud engineer – $130,000+
- Cloud architect – $147,000+
- Cloud AI/ML engineer – $163,000+
- AI/ML architect – $175,000+
As you can see, cloud architects and cloud AI engineers lead the way in earning potential. While salaries will vary depending on your experience, certifications, and region, demand across all five roles is strong.
What about career growth?
All five roles offer excellent long-term growth:
- Cloud engineers often move into senior engineering, cloud security, or architecture roles
- DevOps engineers advance to roles like SRE, DevOps architect, or CTO
- Cloud architects may move into enterprise architecture or consulting
- Cloud AI/ML engineers transition to MLOps leads or senior AI developers
- AI/ML architects step into head of AI engineering, platform architect, or strategic cloud AI roles
No matter your starting point, many professionals pivot into other roles as their experience and interests evolve..
How to choose the right path
Here’s a simple way to decide based on your interests and strengths:
- Like building systems and infrastructure? → Cloud engineer
- Prefer strategic thinking and systems design? → Cloud architect
- Love automation, scripting, and pipelines? → DevOps engineer
- Passionate about data, AI, and machine learning? → Cloud AI/ML engineer
- Already have AI/ML and cloud experience and want to lead system design? → AI/ML architect
No matter where you start, cloud computing is the foundation for all these paths. That’s why many people begin as cloud engineers – it gives you broad exposure to the platform and makes it easier to move into other roles later.
How to train for these roles
Building job-ready cloud skills takes more than watching a few video tutorials or memorizing exam questions. You need guided learning, hands-on practice, and mentorship to apply what you learn in real-world scenarios.
That’s exactly why we’ve created our Cloud Bootcamps at Digital Cloud Training.
These structured programs prepare you for in-demand roles like cloud engineer, cloud architect, DevOps engineer, or cloud AI/ML engineer. You’ll build real-world projects, gain hands-on experience, work in groups, and get certified along the way.
The bootcamps give you a clear roadmap from where you are now to where you want to be – fully equipped with the hands-on skills, experience, knowledge, and confidence to succeed in the job market.
Final recommendation
If you’re just starting out, begin with cloud engineering. It gives you a solid foundation, helps you learn the core cloud services, and opens up paths into DevOps, architecture, or AI later.
If you already have experience in software development or data science, you may be ready to pursue DevOps, Cloud AI/ML, or AI/ML architecture roles directly.
But whichever direction you choose, remember:
Certifications get you interviews. Real-world skills get you hired.
If you’re ready to get both, apply for the Cloud Mastery Bootcamp today.
Not sure which path is right for you? Book a discovery call with our team and we’ll help you choose the best program for your goals and background.