Indian techie who worked with Google, Amazon shares tips ‘to land a job offer in the AI era’: ‘Build a portfolio of…’

Hazarika said that while fundamentals such as data structures, algorithms and system design are still important, they are no longer enough on their own. These skills, he explained, are now treated as the bare minimum. With AI tools widely used for writing code, reviewing pull requests, and even assisting with design decisions, companies—especially startups—expect candidates to demonstrate far more than textbook knowledge. “AI is now widely used for coding, review, and design, so tech companies, especially startups, expect more from candidates,” he noted.

He shared that he personally relies on AI to generate boilerplate code, which allows him to spend more time thinking through complex architecture and business logic. Because of this shift, engineers are now expected to know how to work with AI–from prompt engineering and AI-driven debugging to handling errors and deciding when an AI-based solution actually makes sense.

“You’re still expected to have fundamental knowledge of core system design, data structures, and algorithms. You can still expect interviewers to test your problem-solving approaches, and if you know how to make the correct tradeoffs in time and space. Interviewers still care about debugging skills, since AI makes a lot of fundamental logic errors,” Hazarika said.

He also pointed out that some companies have begun allowing candidates to use AI tools during live coding interviews. The goal, he said, is not to see whether someone can code without help, but to understand how well they combine sound engineering judgment with AI assistance.

That shift became clear to him during a failed interview in 2024 with a Silicon Valley startup. Despite being explicitly told that he could use AI while working through a large codebase, he chose not to. “That was an eye-opener for me about AI’s new role in this field,” he admitted.

According to Hazarika, system design interviews have also changed. Candidates are now often asked how they would integrate AI into existing systems, manage model lifecycles and weigh trade-offs around cost, reliability and scalability. In some interviews, engineers are handed a small codebase and asked to ship a working feature within an hour, a task he described as “impossible without AI.”

Advice for fresh graduates

When it comes to newcomers, Hazarika stressed the importance of thinking beyond classroom-style preparation. His first piece of advice is to “cultivate a production mindset” by making “open-source contributions on AI or any other GitHub project.” He explained, “This demonstrates that you can navigate a production codebase and work on it independently to build a new feature or fix a bug. For solo repositories, include a README that explains the rationale for your decision.”

He also encouraged graduates to “build a portfolio of AI-integrated projects.” As he put it, “incorporate the use of AI in traditional repositories to demonstrate hands-on experience with AI integration. Don’t just deploy and run it locally – try to deploy it to the cloud. Many cloud providers provide free student credits.”

Another key skill, according to him, is to “master cloud tooling and AI prompting,” by “focusing on prompting AI to drive intended outcomes more effectively by providing structured input and output.” He added, “Skills like AWS or GCP cloud certifications demonstrate your ability to be a keen learner.”

Lastly, he reminded beginners not to abandon traditional practice altogether. “Practice Leetcode-styled questions,” Hazarika advised, saying engineers should “learn different problem-solving patterns and practice these skills regularly. Muscle memory and pattern recognition go a long way.”

Guidance for experienced software engineers

For more seasoned professionals, Hazarika emphasised that experience itself is a major advantage. “Your biggest asset is your deep engineering experience,” he said, before outlining how that experience should now be paired with AI-focused skills. He explained that engineers should map their speciality with complementary AI skills, noting that backend engineers should “focus more on tasks related to scaling systems, especially AI, such as managing throughput and latency during deployment and maintaining versioning.”

For data engineers, he suggested building expertise in tools like “Kubeflow, MLFlow, Apache Spark, and Kinesis,” adding that “these skills are becoming increasingly important.” For those in site reliability roles, he stressed the need to “learn about SRE, which involves tracking AI usage and cost, and building fallback mechanisms when models misbehave in production.”

He also urged senior engineers to think beyond execution and adopt a strategic outlook. “Develop an AI product mindset,” he said, explaining that engineers should “try to understand the trade-offs of relying on a third-party API versus using an open-source one and fine-tuning it for your business purposes.” According to him, “Questions on cost, reliability, and maintainability should always be top of mind as you develop an AI product mindset.”

Finally, he encouraged experienced professionals to start small by improving their current roles. “Make use of AI in your current workplace,” Hazarika advised. He suggested identifying tasks that involve heavy manual effort and experimenting with AI-driven solutions, adding, “You can leverage AI to help you brainstorm on how to make it more efficient.”

Wrapping up his reflections, Hazarika said the engineers who will thrive going forward are those who can balance traditional skills with modern tools. “Don’t just be a pure coder or just a prompt engineer. Be the bridge,” he concluded.