Network Engineer

Network Engineers are predominantly using AI for reducing network complexity. AI and ML-based network analytics play a major role in customizing the network baseline for alerts, lowering noise and false positives. Besides, Network Engineers can recognize issues, threats, and root causes as well. 

On the other hand, Machine Learning based networks can predict traffic flows, develop smart analytics, track network health, and also tighten security measures. In the words of Larry Lunetta , VP of WLAN and Security Solutions Marketing at Aruba, the AI in Network Engineering is all about accessing a large volume and variety of data to train the models that produce reliable results across all networks topologies. This is how AI empowers Network Engineers.

Who is a Network Engineer?

Network engineers can be defined as those professionals who build and maintain the day-to-day operations of computer networks. They deliver a high-availability network infrastructure for sustaining the users’ online and on-site information technology activities. Their roles often overlap with computer network architects or security systems engineers.

Also called Network Architects, their primary goal is to provide maximum network infrastructure, security, and performance to the end users. In short, the prolific bunch decides and configures suitable data communications components to meet the user’s needs.

What does a Network Engineer do? 

Andreas Grant, Founder of Networks Hardware says that, unlike the myth, Network Engineer doesn’t sit in front of the computer always. They move and interact a lot as they are involved in hardware purchases, their installation, and network configuration.

That’s why if you want to be a Network Engineer in AI, you gotta be strong in data, data science toolbox, domain-specific expertise, and virtual network assistance.


Why is this role in high demand?

A Network Engineer in AI is highly in demand in the present times. According to Bob Friday, Chief AI Officer Juniper and CTO Enterprise, AI affects network engineering in prominent areas: Detecting time series anomalies and Unsupervised Machine Learning. In the coming years, it will also identify bad implementations of the 802.11 specifications and predict user experience by application.

Business enterprises are heavily investing in AI and ML as they have a bigger role to play in future networks and data centers. For example, Ronald Acra, Senior VP,  and Chief Technology Officer at Cisco says his company has a particular server system targeted at supporting AI and ML applications. It has also incorporated several ML techniques to drive networking changes.

The U.S.Bureau of Labor Statistics predicts the growth of Network Engineer jobs at 5%  between 2020 and 2030. 

Also Read: How to Hire a Cloud Network Engineer

Average pay

As per Glassdoor estimates, the national average for a Network Engineer’s salary in the USA is $1,19,297 per year with an added compensation between $2,590 – $56,248.


Network Engineers have an important role in AI-based organizations. They use various data-mining techniques to identify the network entity related to a problem or remove the network itself from risk.

Here are some of the KPIs that help us measure the success of scalable projects in the organization.

  1. Gap measurement
  2. Trait progress
  3. Stochastic algorithm bias
  4. Infrastructure effectiveness
  5. External factors

Job Description

Hiring a talented Network Engineer is dependent on how well you outline the job description. We have just made the job easier for you. Take a look at the Network Engineer job description in the following template:

  1. Install, configure, troubleshoot and support routers, firewalls, and switches (servers, load balancers, storage arrays, network attached storage, network equipment, and related peripherals will be managed by system engineering)
  2. Working knowledge of network equipment like Cisco routers, and switches
  3. Working knowledge of network protocols, technologies, services, and monitoring tools
  4. Manage vendor and/or product provisioning, procurement, and delivery of data services hardware and software
  5. Resource for infrastructure projects for voice/data network design, development, and implementation
  6. Prioritize work based on business criticalities and also be flexible to work on ad hoc tasks as assigned by the manager 
  7. Provide support for branch and home office Network systems including routers, switches, hubs, and Network maintenance and support equipment
  8. Interact with all data vendors to ensure adequate services, support, and maintenance
  9. Troubleshoot system failure, circuit failures, and configurations issues in a production environment
  10. Create and maintain documentation for the network infrastructure

Interview Questions

Sourced from many job websites, these are some of the most asked interview questions for an AI-backed Network Engineer. The questions should be as such that they evaluate the candidate’s proficiency based on all the responsibilities as per the company.

  1. How will you connect two computers for file sharing without using a router or hub?
  2. Explain the measurements that you will take to protect an internal network from external threats.
  3. When a website does not load, how do you troubleshoot the problem?
  4. Define proxy servers. How do they protect computer networks?
  5. Explain the layers of the OSI reference model.
  6. What is the use of encryption on a network?
  7. Explain the common software problems that cause network defects. How do you resolve them?
  8. Name a few networking protocols that you are familiar with.
  9. Mention the pros and cons of private IP addresses.
  10. How do you keep yourself updated with the latest engineering trends?

Best practices for hiring Network Engineer

As AI pushes the boundaries of Network Engineering, hiring managers are also revamping their recruitment strategy. Beyond certification and college degrees, they look at analytical skills and innovative and disruptive solutions.  

Many video interviews suggest that recruiters are struggling to find quality talent for the role as the career path is naive and emerging. While there is an acute talent shortage due to inadequate AI network skills and hands-on experience, it must also be noted that most employers are still stuck in the traditional methods of hiring.

Hiring red flags like over-reliance on credentials than competencies and lack of advanced evaluation tools in skill assessments have severely limited the talent pool.

On the other hand, recruitment software like Glider AI takes candidate evaluation to the next level. Through a structured and standardized process, interviews are made candidate-friendly and also accurately assess skills and competencies. Hiring is not only bias-free but evaluated on real-world scenarios as well.

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