Hello Reader,

I keep hearing the same concern from people who are considering Solutions Architecture:

With AI changing so much of technology, is Solutions Architecture still a good career to pursue?

My answer is that AI is definitely changing the role.

Let’s go through some of the factors, look at some data, and then come to the conclusion.
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AI still needs infrastructure

When companies build Gen AI applications, they still need compute, storage, networking, security, databases, monitoring, scalability, and cost optimization.

The technology on top of the infrastructure is changing quickly, but the underlying architecture still matters.

Most enterprise Gen AI applications are being built using cloud infrastructure, and cloud fundamentals are still important.

Let’s take an example - you create an agent on AWS using Amazon Agentcore. The same AWS principles of security, scaling, monitoring etc. still apply. You still need to know about cloud encryption, authn/z, RBAC to secure that agent.

If you self host the agent, you need to know how to scale the underlying compute effectively for high traffic. And in some cases, if you host the LLM on cloud infrastructure, then the architecture knowledge becomes even more critical.

So learning Gen AI does not mean forgetting everything you already know about cloud.

It means adding Gen AI to your existing foundation.

The role is changing

A Solutions Architect today may need to understand things that were not part of the conversation a few years ago.

No company adopts Gen AI in the vacuum. The AI system needs to integrate with existing data and applications.

How do you integrate an LLM into an existing application? Where should the model run, and how do you secure access to it? How do you manage latency and cost while handling enterprise data and design RAG or agentic workflows well?

These are architectural questions.

You don't necessarily need to become an ML researcher to answer them.

You need enough Gen AI knowledge to make good architectural decisions.

Don't make the mistake of chasing every AI trend

This is where I see many people getting distracted.

A new framework comes out. Then another model. Then another agent framework. Then MCP. Then another AI tool.

You can spend all your time learning the latest technology and still lack the fundamentals required for a Solutions Architect role.

I've written before about how the Gen AI landscape keeps changing. What is popular today may be abstracted or replaced tomorrow.

Instead, I recommend a T-shaped approach.

Breadth: Understand the fundamentals of Gen AI and how it fits into modern applications.

Depth: Connect Gen AI to the area you already know.

If you know Kubernetes, learn how AI workloads run on Kubernetes. If you know Serverless, learn how it can support AI applications. If you know storage, learn about vector databases and embeddings. If you come from DevOps, explore MLOps and AI infrastructure.

This way, you're extending your existing expertise instead of starting from zero every time a new technology appears.

What still makes a good Solutions Architect?

The fundamentals haven't disappeared.

You still need to design systems, weigh trade-offs, and think about security, scalability, reliability, and cost. And just as important you need to communicate those decisions clearly.

That last part is particularly important.

A Solutions Architect is not hired simply because they know the names of 200 cloud services.

They need to understand a business problem, design a solution, explain the trade-offs, and communicate with both technical and non-technical stakeholders.

AI can help you do some of that work faster. But you still need to know what should be built and why.

So, should you still pursue Solutions Architecture?

The more chaos there is, the more the need for Solutions Architects. If everything was straightforward, and there are no alternative services or solutions, there’d be no need for Solutions Architects. And for better or worse, the chaos is at max right now.

I would not build your career around trying to compete with AI.

Build your career around understanding technology deeply enough to use AI effectively.

Learn cloud fundamentals and system design, understand how businesses use technology, and get good at communicating architectural decisions. Then add Gen AI to your skill set.

There are over 70,000 open Solutions Architect positions, and the numbers are growing every month, which proves that Solutions Architects who know Cloud + Gen AI are here to stay. And lastly - preparing for Solutions Architects also open you upto many Gen AI roles, including FDE.

The tools will keep changing. The ability to solve complex problems and make good architectural decisions will remain valuable.

Keep learning and keep rocking 🚀,

Raj

P.S - If you want to get an AWS Solutions Architect job without coding or learning every AWS service, the 10th cohort for AWS SA Bootcamp is launching on Oct 17th, 12 PM ET (Eastern Time) via live workshop. This program now includes our updated Gen AI curriculum for FDE roles. Please register below:

Here’s what you get when you show up LIVE:

1. The myths keeping most people stuck - and what actually gets you hired as an SA - I've conducted over 300 SA interviews, so I know what I'm talking about!
2. How GenAI is reshaping the SA and Gen AI roles including FDE, and the exact AI concepts (RAG, agents, MCP, eval etc.) you need to speak fluently in interviews.
3. A first look at my new product feature, built to help you practice real-world, interview-relevant hands-on work instead of copy-paste tutorials.
4. Full bootcamp breakdown for Cohort 10, plus a special offer only for live attendees.
5. My exclusive Solutions Architect framework to prep you for today's job market! But if you’re not live, you won’t get it. No second chances.

And good news - it already worked for last cohort's students who secured cloud jobs in top companies, including at AWS, Microsoft, Google, JPMorgan, Reddit, and some of them didn't even have cloud experience 💰.

Spots are limited, so don't miss it!