If you’re a DevOps engineer or aspiring to become one, you’ve probably asked yourself: what’s next? In an ever-evolving tech landscape, where roles like SRE, platform engineering, AIOps, and MLOps are gaining prominence, mapping out your career path can feel overwhelming. With 17 years of experience in DevOps and over 10,000 hours of training professionals, I’ve created a clear, actionable five-year roadmap to help you navigate these decisions.
The 0-to-1 Journey: Building Strong Foundations
For beginners, the journey starts with mastering Linux, networking, and a major cloud platform like AWS or Azure. These foundational skills form the backbone of any DevOps role. Add tools like Terraform for infrastructure automation and scripting to your skill set, and you’re well on your way to entry-level roles like Cloud Engineer.
Key takeaways for this phase:
Focus on learning essential cloud technologies, such as networking (VPC), compute (EC2), storage (S3), and databases (RDS).
Experiment with hands-on projects to demonstrate your knowledge.
Use certifications like AWS Certified Solutions Architect as a qualifier but prioritize real-world skills.
From CloudOps to DevOps Engineer
The transition to a DevOps Engineer role involves adopting core DevOps practices such as:
Version control with Git.
Containerization with Docker and orchestration with Kubernetes.
CI/CD pipelines with Jenkins, GitHub Actions, or ArgoCD.
Many overlook Jenkins as “outdated,” but I recommend starting with it for its robust, job-relevant features. Once you understand these fundamentals, learning modern tools becomes easier—a process I call transfer learning.
Beyond DevOps Engineering: Specialization Paths
Once you’re a DevOps Engineer, the real growth begins. Here are three advanced paths to consider:
Platform Engineering
Ideal for those who enjoy programming and building developer-friendly tools, this specialization focuses on creating internal platforms and optimizing workflows. Think self-service Kubernetes environments or Slack-integrated access management systems.
AIOps-Powered SRE (Site Reliability Engineering)
As AI becomes a central part of operations, SRE roles now emphasize automation and predictive analysis. With AIOps, you’ll leverage machine learning for:Advanced observability (e.g., distributed tracing).
Predictive maintenance to prevent failures.
Automated incident resolution
.
AI Platform Engineering (MLOps)
If you’re intrigued by AI, MLOps is about building and managing the infrastructure powering AI applications. From GPU-optimized Kubernetes clusters to observability for machine learning pipelines, this role blends DevOps practices with AI-specific needs.
AI and the Future of DevOps
One of the most common questions I hear is: Will AI replace DevOps roles? My answer: No, but it will redefine them. By aligning yourself with AI-powered tools and practices, you’ll remain not just relevant but indispensable. The best way to thrive in the AI era is to get behind the infrastructure and platforms enabling AI.
Your 5-Year Roadmap at a Glance
Year 1: Focus on foundational skills like Linux, networking, and cloud essentials.
Year 2-3: Transition to a DevOps Engineer role by mastering Git, Docker, Kubernetes, and CI/CD.
Year 4-5: Choose an advanced specialization (Platform Engineering, AIOps, or MLOps) based on your interests and strengths.
Want to dive deeper? Visit https://schoolofdevops.com/ for learning programs.
Join the conversation! What career path excites you the most? Let me know in the comments. Join our community of Devops Builders on Reddit.
This is Gaurav Shah from School of DevOps—helping you survive and thrive in the age of AI and beyond.
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