I've worked at Google, Amazon, and Salesforce. Here's how to prep for an interview in the AI era, no matter your experience level.

• Akaash Vishal Hazarika shares how AI is reshaping the skills required for software engineering jobs.
• Tech companies now expect engineers to integrate AI into coding, debugging, and workflows.
• He suggests candidates combine core engineering skills with AI expertise for career success.

This as-told-to essay is based on a conversation with Akaash Vishal Hazarika, a 29-year-old senior software engineer based in Seattle. It's been edited for length and clarity.

I've been a Big Tech employee for the past eight years, working at Google, Amazon, Splunk, and now Salesforce. I've had a front-row seat to witness the changes in the tech landscape.

I've learned which skill sets software engineers need to land a job offer in the AI era. Tech companies agree that AI makes engineers more productive, so engineers are expected to use it to build things more quickly and reliably.

I personally make heavy use of AI to help me with boilerplate stuff so that I can concentrate on the hard stuff, like system design and complex business logic.

Preparing to be a software engineer isn't the same as it was five years ago

When I was interviewing for software engineering jobs in 2020, LeetCode and system design were the de facto standards for cracking a job interview. Job seekers who had an advanced understanding of data structures and algorithms would come out on top.

Today, this is just the baseline expectation. AI is now widely used for coding, review, and design, so tech companies, especially startups, expect more from candidates.

Some skills remain the same: Software engineers still need a problem-solving mindset and should know how to scale systems and leverage cloud services. New hires must now also understand prompt engineering — how to leverage AI to code a solution more efficiently.

You need to be able to use AI for error handling and bug fixing. AI systems integration is another important new skill that involves incorporating AI into existing workflows, scaling AI systems, and determining when to use traditional versus AI solutions for business decisions.

Interviews have also evolved with the arrival of AI

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.

You also need to be prepared for some new things. I've seen firsthand that working with AI assistants in live screen-sharing mode is now allowed in some interviews. Interviewers are looking to see if you can help them achieve their business outcomes by combining software engineering skills with AI prompting.

In an interview I had with a Silicon Valley startup in 2024, I expected the hiring team to give traditional coding challenges. I was taken aback when I was given a huge code file and asked to debug a buggy behavior, and the interviewers explicitly said I could use AI assistance.

I ignored the invitation to use AI, thinking I was supposed to do it myself, and ended up spending a lot of time on the problem to no avail. I failed that interview. That was an eye-opener for me about AI's new role in this field.

You may also be asked system design questions about where AI should be integrated into the current business workflow, or to discuss the trade-offs of using AI and traditional approaches in various problem contexts.

During my own system design interviews, I noticed companies starting to ask questions about how to integrate AI to enhance existing systems and support a particular model lifecycle in terms of infrastructure. What I've seen is that companies give access to a small codebase and expect you to deliver a small feature in about one hour, which is impossible without AI. With AI, you can roll it out easily.

I've also been asked behavioral questions, like "How do you plan to evaluate the use of AI in order to make the workflow better?" and "How do you plan to balance automation and manual oversight?"

For new graduates, here's how to show hiring teams that you're equipped for the emerging needs of the software engineering role.

1. Cultivate a production mindset

Start by contributing to open-source projects on AI or any other GitHub project. 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.

2. Build a portfolio of AI-integrated projects

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.

3. Master cloud tooling and AI prompting

Focus on prompting AI to drive intended outcomes more effectively by providing structured input and output. Skills like AWS or GCP cloud certifications demonstrate your ability to be a keen learner.

4. Practice Leetcode-styled questions

Learn different problem-solving patterns and practice these skills regularly. Muscle memory and pattern recognition go a long way.

For experienced software engineers, your biggest asset is your deep engineering experience — here are three tips that will help you in 2026 and beyond.

1. Map your specialty with complementary AI skills

Pairing years of engineering expertise with impressive AI skills will get the attention of hiring managers. Consider boning up on these complementary skills:

• Backend: Focus more on tasks related to scaling systems, especially AI, such as managing throughput and latency during deployment and maintaining versioning.
• Data engineering: Build knowledge and skills in Kubeflow, MLFlow, Apache Spark, and Kinesis — these skills are becoming increasingly important.
• Site reliability engineering: Learn about SRE, which involves tracking AI usage and cost, and building fallback mechanisms when models misbehave in production.

2. Develop an AI product mindset

Senior software engineers don't just build; they also need to strategize and innovate. 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. Questions on cost, reliability, and maintainability should always be top of mind as you develop an AI product mindset.

3. Make use of AI in your current workplace

Identify workflows that have a great deal of manual involvement in your current job, and use AI to see if you can improve them. You can leverage AI to help you brainstorm on how to make it more efficient.

Both veteran and newbie engineers should stay curious: find out the cool technologies and AI agents available in today's market, but don't abandon fundamental engineering approaches. The hybrid understanding of these technologies is what makes you valuable.

If I were interviewing, I'd position myself as a 'hybrid engineer.' Don't just be a pure coder or just a prompt engineer. Be the bridge.