What are The Skills Needed In The AI Hiring Era? Google Techie Shares Insights

 An Indian software engineer who has worked for some of the largest tech companies in the world for almost ten years, including Google and Amazon,has discussed how, in the era of artificial intelligence (AI), job interviews and the abilities required to ace them have completely altered.

Akaash Vishal Hazarika, a 29-year-old senior software engineer located in Seattle, discussed his eight-year career at Google, Amazon, Splunk, and Salesforce in an as-told-to piece published by Business Insider. He provided thoughts on how software engineers can now prepare for interviews in the artificial intelligence era.

'Traditional Skills Like Data Structures are Not Needed Now'

"I have been able to see firsthand how the tech industry has changed. "I now know what skill sets software developers need to get a job offer in the AI era," he remarked.

Hazarika claims that conventional preparatory techniques, like learning data structures, algorithms, and system architecture, are insufficient.Although these principles are still crucial, he claimed that they are now regarded as standard expectations. "Tech businesses, especially startups, expect more from candidates because artificial intelligence is increasingly commonly utilized for coding, review, and design," he said.

Companies Use AI Tools Nowadays

According to Hazarika, he uses artificial intelligence a lot for boilerplate code so that he can concentrate on intricate system architecture and business logic. Because of this, engineers are needed to comprehend mistake handling, prompt engineering, AI-assisted debugging, and determining when AI solutions make more sense than conventional methods.

It is nevertheless assumed that you have a basic understanding of data structures,algorithms, and core system design. Interviewers will still assess your problem-solving techniques and your ability to make the right time and space trade-offs. Because artificial intelligence frequently makes basic logical mistakes, interviewers are still interested in debugging skills, according to Hazarika.

Everyone Should Now Need AI For Learning

Hazarika continued by recalling that she had failed an interview with a Silicon Valley startup in 2024 because she had disregarded clear authorization to employ artificial intelligence while debugging a sizable codebase. He wrote, "That opened my eyes to artificial intelligence's new role in this industry."

According to Hazarika, concerns like integrating artificial intelligence into current workflows, managing model lifecycles, and assessing trade-offs, including cost, dependability, and scalability, are now frequently included in system design interviews. He stated that it is "almost impossible without artificial intelligence" for candidates to produce a feature in an hour when given a tiny codebase.

What Are The Skills Freshers Need To Get Into AI Field

 To gain a solid foundation in fields like NLP, computer vision, and ML engineering, newcomers to artificial intelligence should master Python programming, core Maths & Stats (linear algebra, probability), basic Machine Learning concepts, Data Handling, and important libraries like TensorFlow/PyTorch. They should also develop soft skills like problem-solving, communication, and continuous learning. 

Fundamental Technical Skills Programming:

Python is necessary (with libraries like NumPy, Pandas). Java and R are also useful.

• Math & Statistics: All artificial intelligence algorithms are based on Linear Algebra, Calculus, Probability, and Statistics.
• Learn the fundamentals of machine learning (ML), including supervised and unsupervised learning, neural networks, and model training.
• Deep Learning: Acquire knowledge of Deep Learning frameworks (PyTorch, TensorFlow) for sophisticated applications.
• Data handling includes preprocessing, analysis, visualization, and data cleaning (SQL is also essential). 

Important Artificial Intelligence Specializations

Natural language processing, or NLP, is the process of comprehending and producing human language.

• Computer Vision: For activities involving the comprehension of images and videos.
• Generative AI: Pay attention to prompt engineering and generative models. 

Crucial Frameworks & Tools

• ML Libraries: Keras, Scikit-learn.
• Big Data: Apache Spark (for big datasets).
• Cloud Platforms: Knowledge of AWS, Azure, or GCP is advantageous.

Hazarika concluded by advising engineers to market themselves as "hybrid engineers." "Be more than just a prompt engineer or a pure coder. He said, "Be the bridge."