From Amazon Intern to Staff Engineer: Building an AI-Resistant Multi-Domain Career

A deep conversation with Aniket Chakravarty, who evolved from Amazon intern to Staff Engineer across 5 domains in 8 years. His strategic approach to building AI-resistant careers through domain expertise.

July 17, 2025

5 min read

By Rachit Magon

The era of single-domain engineering careers is ending. While 75% of developers now use AI coding tools daily, the real question isn't whether AI will replace engineers - it's which engineers will become irreplaceable.

We sat down with Aniket Chakravarty, a Staff Engineer who cracked this code differently. Starting as an Amazon intern in 2016, he didn't just climb the technical ladder - he systematically conquered five different business domains: banking, e-commerce, customer service, construction tech, and more. His strategy? Understanding not just how to build, but what to build and why.

Here's his blueprint for building an AI-resistant career through strategic domain expertise.

Key Takeaways: Your Strategic Career Evolution Roadmap

Immediate Actions (Next 30 Days):

• Start using AI tools strategically: GitHub Copilot for boilerplate, but understand every line
• Join design discussions in your current role - focus on the "why" behind features
• Begin learning one adjacent business domain deeply

Medium-term Strategy (6-18 Months):

• Move from feature-focused to product-focused thinking
• Practice architectural decision-making and cross-domain pattern recognition
• Build relationships across different business functions

Long-term Vision (2-5 Years):

• Position yourself as a domain expert who can architect solutions, not just implement them
• Develop the ability to translate business requirements into technical strategy
• Become the human who provides oversight and direction to AI-generated solutions

Q: Take us back to 2016 when you started your Amazon internship. Did you have any idea you'd end up mastering multiple business domains?

Aniket: Certainly not. When I was an intern at Amazon, I had very different hopes and future plans. I practically had no idea where my career would go at that time.

But today, looking at the journey I've made - the companies, the domains I've learned along the way - I've picked up so many friends and mentors who have actually helped me achieve whatever I've accomplished till now. It's been great overall.

During my internship, there were so many things that were completely new to me - technologies, architecture, and especially scale. Scale is something you might see for the first time after you get into a big company like Amazon. That was a great learning experience.

🔥 ChaiNet's Hot Take: This organic approach to career growth is becoming more valuable in the AI era. While 60% of engineers follow linear career paths, those who embrace cross-domain learning are positioning themselves for the 40% of senior roles that require business domain expertise alongside technical skills.

Q: You've worked across different scales - from Amazon's massive infrastructure to startups. What's the key difference in what you learn at each?

Aniket: I've worked in both big and small companies, service-based and product-based - different types. Every place has something different to offer.

Companies that are service-focused have a lot of process-oriented work. You need to be compliant with many things - you have to think about how whatever changes you're making will impact the customer or client directly. Compliance is a major part there.

In startups, you basically focus on time to market - how fast you can build, how soon you can take your application to customers. Every stage has different things to learn, and I've been grateful for all the experiences.

🔥 ChaiNet's Hot Take: This multi-environment experience is crucial for AI resilience. Startup velocity teaches you to ship fast with AI tools, while enterprise experience teaches you the governance and oversight that AI systems desperately need. The combination is powerful.

Q: You've moved to a Staff Engineer position. What's different about your mindset now versus 3-5 years ago?

Aniket: The major difference is the focus of the developer. Initially, we're focused on the feature or application we're developing. But as we grow, we become more product-focused instead of feature-focused.

We need to think about what the product actually wants, what the customer actually wants, and what things can arise in the future. We have to keep those things in mind. Our focus shifts from lower-level implementation to higher-level design, architecture, and scale - whether that will support our system or not. A completely different perspective is required once you get to a staff engineer level.

🔥 ChaiNet's Hot Take: This shift from "how to build" to "what to build" is exactly what makes engineers AI-resistant. While AI excels at implementation, the strategic thinking about product direction, customer needs, and system architecture remains distinctly human.

Q: Let's talk about AI coding tools. We know 75% of developers use them, but what's actually changed in day-to-day work for someone shipping real products?

Aniket: There's definitely a huge impact on developers' lives since these tools came up. I personally use them every day.

The number one thing is how people search for code - they've basically replaced Stack Overflow and Google searches. You don't need to change your IDE or anything, you just type what you want to search and get an answer.

Apart from that, whatever mundane or repetitive tasks we do, those have been replaced. If I want to make an application, there's a lot of boilerplate code initially that I have to write - those can be delegated to AI agents and Copilot. Unit tests can also be done, along with refactoring code or simplifying complex logic.

One thing that definitely helps is with legacy code. People struggle with legacy code and don't want to touch it. With these tools, at least we can do some simplification that we can change later. Developers can focus on business logic and creative problem-solving.

🔥 ChaiNet's Hot Take: The productivity gains are real - GitHub reports 55% faster code completion with Copilot. But Aniket's emphasis on "creative problem-solving" is key. The engineers who survive will be those who use AI as a force multiplier for higher-level thinking, not a replacement for understanding.

Q: There's a concern that junior developers are shipping faster but not learning fundamentals. Are AI tools creating better developers or just faster code producers?

Aniket: You're definitely right. The problem-solving has been replaced by productivity - how fast you can ship. Critical thinking has taken a hit, but that's the whole point of your current episode, right? How not to get replaced by AI - that's what will make the difference.

If somebody is just using AI to ship code without understanding what's happening internally, their job is probably under threat. But people who are understanding the code definitely have better scope in the future.

Let me give you an example. I was using Bolt to create a multi-page frontend application recently. Bolt was rendering pages based on if-else statements. I had to specifically tell it to use React Router instead. That's very basic, but AI probably doesn't understand that because statistically, if-else might produce better results in their training data.

If the developer doesn't know what exactly they're writing, whether it's right or scalable for the future, it will come back and bite them.

🔥 ChaiNet's Hot Take: This is the crux of AI-resistant career building. Tools like Bolt and Cursor are powerful but make assumptions based on training data, not architectural best practices. The engineers who understand these limitations and can guide AI toward better solutions will remain indispensable.

Q: How should someone balance using AI tools versus understanding fundamentals, especially under deadline pressure?

Aniket: There's no right answer - you need balance. You can use AI to write code, but you need to understand what it's actually doing. If in the future you need to come back and make changes, and you don't have the same tool with the same context, you'll have to understand what it has done.

You need complete understanding of what you've shipped, regardless of whether it's written by you or AI. Beyond understanding what AI has written, you need to know what could have been better. There can be scenarios where the AI solution is probably not optimal.

It comes down to the individual - they need to spend time understanding whether whatever they're shipping is something they completely understand. If not, they should take a step back, go through it properly, and then take the next step.

Q: How have code reviews changed with AI-generated code? What do you look for now?

Aniket: There are lots of bots you can add to GitHub Actions that can do code reviews for you. But what they're doing is very basic level - things a human might miss, like debug logs that should be removed or type mismatches.

We should rely on AI for those generic things, but at a certain point, you need to understand that AI won't know the complete codebase. They won't know whether there was a utility function already existing or if I should write a new one - they'll probably write a new one every time.

Sometimes AI will have the best possible solution, but you might be following a different pattern in the rest of your code. I specifically do reviews where I look for repetitive patterns that we can replace instead of using AI-generated outputs.

You need to work with AI in tandem - you can't say you'll completely avoid AI for reviews or completely use AI. The reviewer and AI both need to provide input.

🔥 ChaiNet's Hot Take: This tandem approach is becoming the new standard. Companies using AI-assisted code reviews report 40% faster review cycles, but human oversight remains critical for architectural consistency and codebase coherence.

Q: Where do you draw the line on what can and cannot be done with AI?

Aniket: We don't have a guideline for that, and I don't think we can have a guideline because ultimately, if individuals are using AI for something we don't know about, we cannot control everything.

The main idea is that before somebody writes code, we go through design discussions. Those discussions will help determine what you're going to write and how you're going to write it. Instead of AI generating the complete pattern, logic, and framework, you should have a framework already in mind.

If a person has an idea of what needs to be done, then if they use AI, it's well and good because they'll be able to catch what AI did wrong and modify that.

I specifically encourage unit testing using AI because when we write as humans, we might have one happy path, two or three edge cases, and one failure path - very limited. When AI writes it, it can think of more scenarios, so the number of test cases increases and your code has better quality.

Q: Do you think QA roles will be completely replaced by AI?

Aniket: I don't think anyone can be completely replaced by AI. There will definitely be changes in how things are done. Instead of doing testing manually, QA might be writing prompts for AI to generate tests and complete test suites.

Things will change, but nobody's going to be replaced directly. You have to mold yourself with the change. If you're adopting and embracing whatever new technology comes in and how work is changing, nobody can replace you.

Q: In your experience, does AI sometimes introduce more technical debt than it saves?

Aniket: I haven't seen much tech debt getting introduced by AI, as I said - we focus a lot on what's being generated during code reviews. But it's definitely true that if somebody is shipping without understanding what's happening, it will come as a bug or tech debt later.

There's another side to it though - whatever tech debts we used to push for later, with the speed AI provides, we can actually complete those as well. Whatever time you save, you can utilize that. You can complete the feature as well as handle pending work together.

🔥 ChaiNet's Hot Take: This is a crucial insight. While poorly managed AI adoption can create technical debt, strategic use can actually help address existing debt faster. The key is maintaining architectural oversight while leveraging AI's speed for cleanup tasks.

Q: How has the interview process changed with AI tools that can help candidates cheat?

Aniket: With these tools, I've seen lots of scenarios where people seem like they're reading from chat or typing somewhere with multiple screens open. There's no absolute way to determine this, but the easiest way is to understand whether the answers they're giving are human or not.

You can detect if answers are very monotonous and give specific definitions instead of showing understanding. The way candidates conduct themselves in the interview can tell you a lot about whether they're cheating or not, but there's no deterministic way to differentiate.

With how interviews are changing, people will probably start calling candidates to the office more often for face-to-face interviews.

Q: What's your prediction for AI in software development over the next 2-3 years?

Aniket: I definitely think there's a possible future where you give a product requirement to AI and it becomes a series of agents - one generates the design, another reads requirements and starts writing code, maybe a master or supervisor agent that checks what others are doing.

I'm pretty sure there will be a future with this kind of swarm of agents doing things end-to-end. Not one system doing everything, but a swarm helping each other like humans help each other. You have a product manager, designer, backend, frontend, QA - you'll have different agents communicating with each other.

Ultimately, there needs to be accountability, so there will be human intervention at each step to check and verify that things are happening correctly and going in the right direction. That's how you can retain your job - get into positions that understand the output of AI systems.

🔥 ChaiNet's Hot Take: This agent swarm vision aligns with recent developments. Companies like Cursor and Replit are already building multi-agent coding systems. The key insight: human oversight and orchestration become the critical skills, not the implementation itself.

Q: If you could speak to that Amazon intern in Hyderabad right now, what one piece of advice would you give?

Aniket: Don't become a software engineer.

[laughs]

That's something - I think the software engineering scenario is quite scary right now. I'm definitely positive that things won't turn that bad, but you never know.

🔥 ChaiNet's Hot Take: This brutally honest advice reflects the industry reality. While dramatic, it emphasizes that traditional software engineering roles are transforming rapidly. The survivors will be those who evolve beyond pure coding into strategic, domain-aware professionals who can direct and orchestrate AI systems.

The Strategic Path Forward

Aniket's journey from Amazon intern to Staff Engineer across multiple domains offers a clear blueprint for building an AI-resistant career. The key isn't avoiding AI tools - it's positioning yourself as the strategic brain that guides them.

His advice boils down to three critical elements:

1. Domain Expertise: Understand the business context behind the code you're writing
2. Strategic Thinking: Move from "how to build" to "what to build and why"
3. AI Partnership: Use AI as a force multiplier while maintaining architectural oversight

The future belongs to engineers who can think beyond implementation - those who understand customer needs, business requirements, and system architecture deeply enough to direct AI tools toward optimal solutions.

As the industry transforms, the question isn't whether you'll work with AI - it's whether you'll direct it or be directed by those who do.

Connect with Aniket: You can find him on LinkedIn for career guidance and insights into navigating the evolving tech landscape.

The engineering landscape is changing faster than ever. The professionals who adapt strategically - building domain expertise while leveraging AI tools intelligently - will define the next generation of tech leadership.