Will AI replace a IP Network Engineer?
AI risk 55/100Opportunity 85/100Future demand 75/100
How AI is affecting this role
- ›Instead of manually grepping through 10,000 lines of logs to find why a BGP session dropped, the engineer uses an LLM to analyze the dump and pinpoint a mismatched AS number in seconds.
- ›Using a prompt like 'Write an Ansible playbook to update NTP servers on 50 Cisco IOS devices,' the engineer generates the code, reviews it, and deploys it in under 5 minutes.
- ›An AIOps tool detects a gradual increase in latency on a critical link before it impacts users, automatically rerouting traffic, and the engineer only validates the change rather than fighting the fire.
- ›The engineer uploads a PDF of a new network design policy to Claude, asks for a checklist of CLI commands to implement it, and gets a 90% accurate draft immediately.
Ways to survive
- ›Stop treating CLI as your primary skill; treat it as a fallback.
- ›Learn to read AI-generated code critically—knowing when a suggested Python script will crash the network is your new job security.
- ›Adopt AIOps tools provided by your vendors (Cisco/Juniper/Nokia) before they are imposed on you.
Ways to get ahead with AI
- ›Build internal tools that translate plain English requests (e.g., 'Block Youtube for Sales VLAN') into specific firewall rules using OpenAI's API.
- ›Use Python and AI to automate the 'network compliance audit' process, selling this as a time-saving tool to management.
- ›Master 'Generative Adversarial Networks' (GANs) concept to simulate network traffic for better stress testing.
How ONROL helps
We will help you transition from CLI-based engineering to building autonomous network agents. Our courses focus on Python for Network Engineers and AIOps implementation, specifically tailored to environments using Cisco, Juniper, and Nokia gear common in India.
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