DevOps : Entry-Level vs Mid vs Senior
This is how your Cloud, DevOps & AI toolset evolves as you gain experience…
➤ Entry-Level (0–2 years)
• SDLC Concepts
• Linux & Shell Scripting
• Docker & K8s (Intermediate level)
• Basic Git workflows
• Foundational DevOps concepts (CI/CD | Virtualization | Containerization)
• Troubleshooting skills (fixing broken pipelines, reading logs)
• Jenkins / GitLab CI basics
• AWS / GCP / Azure / OCI core services
• Intro to AI workloads on cloud (deploying simple ML models, using prebuilt APIs)
➤ Mid-Level Cloud/DevOps Engineer (3–6 years)
• Kubernetes for orchestration
• Terraform for infrastructure automation
• Ansible/Chef for configuration management
• Logging Stack (Datadog / ELK)
• Monitoring Stack (Datadog / Prometheus / Grafana)
• Python/Go automation for end-to-end workflows
• AWS/GCP/Azure/OCI advanced services
• AI Infrastructure basics (GPUs/accelerators, inference endpoints, model-serving tools)
• Working with vector DBs / feature stores in ML pipelines
➤ Senior Cloud/DevOps Engineer (7–10 years)
*Same tools, but with architectural ownership*
• Infrastructure for scalability & reliability
• Security by design
• DevSecOps implementation strategies
• Cloud migration planning & architecture
• Service Mesh implementation & management
• Cost optimization at scale
• Cross-cloud / hybrid patterns
• Platform engineering
• Designing infra for AI systems (distributed training, inference scaling, latency tuning)
• AI Observability (model drift, reliability, performance tracing)
