My Journey from Full-Stack Engineering to AI-First FDE

Hi everyone! 👋

I’m Prakash Tank, a Full-Stack Architect and AI Tech Lead with 10+ years of experience building production software.

Over the years, my role has changed quite a bit.

I started with a strong focus on backend and full-stack development. Then I moved into architecture, started leading teams, and became more involved in solving business problems, not just writing code.

Today, I’m focused on building AI-powered products and working as an AI-first Forward Deployed Engineer (FDE).

This post is about that journey and how I think about software engineering today.

It Started With Full-Stack Development

For many years, my work has been around building real-world applications.

I have worked with technologies like:

• Laravel and PHP
• Node.js and TypeScript
• React and Next.js
• MySQL and PostgreSQL
• Redis and Kafka
• AWS
• Docker
• APIs and distributed systems

But working on production applications taught me something important:

Writing code is only one part of building software.

A good solution starts before the code.

You need to understand:

• What problem are we solving?
• Who is facing the problem?
• Why does the problem exist?
• What does the existing system look like?
• What is the simplest solution that can actually work?
• How will we maintain and scale it?

This changed the way I approached engineering.

From Developer to Tech Lead

As I gained experience, I started taking more responsibility beyond my own code.

I started working closely with teams, reviewing architecture, making technical decisions, breaking down complex requirements, and helping other developers solve difficult problems.

That eventually led me into a Tech Lead role.

Being a Tech Lead taught me another important lesson:

A Tech Lead is not just the person who writes the most code.

A good Tech Lead helps the whole team move in the right direction.

My responsibilities became things like:

• Understanding business requirements
• Designing technical solutions
• Making architecture decisions
• Breaking large problems into smaller tasks
• Reviewing code and technical approaches
• Helping developers when they are blocked
• Improving development processes
• Thinking about performance, security, and scalability
• Communicating technical decisions with stakeholders

This experience became very valuable when I started working more deeply with AI.

Then AI Changed the Way I Think About Software

AI didn't make me stop being a software engineer.

It changed what I could build.

Instead of thinking only about traditional APIs, databases, CRUD operations, and business logic, I started asking different questions.

Can AI solve this problem?

Can an existing workflow become smarter?

Can users interact with the system in a more natural way?

Can we automate part of this process?

This led me to explore technologies and concepts around:

• Large Language Models (LLMs)
• RAG
• AI agents
• AI-powered workflows
• Local LLMs
• Ollama
• AI integrations
• AI-powered SaaS products

But I quickly realized that building an AI feature is not simply about calling an LLM API.

The difficult part is still the same:

Understanding the real problem.

This Is Where FDE Makes Sense to Me

The Forward Deployed Engineer approach strongly matches how I already like to work.

For me, an FDE is someone who can go from:

Problem → Understanding → Architecture → Prototype → Production → Iteration

You don't just receive a ticket and implement it.

You understand the environment first.

You look at the existing product, data, infrastructure, users, and business workflow.

Then you decide where technology, and now AI can actually create value.

Sometimes the right solution will be an AI agent.

Sometimes it will be RAG.

Sometimes it will be a simple API.

And sometimes the answer is not to use AI at all.

That last part is important.

Using AI everywhere doesn't make a product AI-first.

Using AI where it actually solves a problem does.

AI-First Does Not Mean AI-Only

This is something I strongly believe.

An AI-first engineer still needs strong software engineering fundamentals.

An AI application still needs:

• Good architecture
• Reliable APIs
• Databases
• Authentication
• Authorization
• Logging
• Monitoring
• Security
• Testing
• Deployment
• Scalability

An LLM response alone is not a production system.

You need to build the system around it.

That's where my full-stack and architecture experience becomes useful.

Building AI Into Real Systems

I'm particularly interested in bringing AI into existing business applications.

For example, imagine an enterprise system that already has years of business logic, databases, workflows, and users.

The goal shouldn't always be to replace everything with AI.

Instead, we can ask:

Where can AI make the existing system better?

Maybe AI can help users find information faster.

Maybe it can understand documents.

Maybe it can assist with complex workflows.

Maybe it can summarize large amounts of data.

Maybe an AI agent can perform repetitive tasks.

Maybe natural language can become another interface for an existing application.

This is the kind of AI engineering that interests me.

AI that works with real software and real business processes.

My Role Today

Today, I see myself at the intersection of several areas:

AI-first Forward Deployed Engineer

Full-Stack Architect

AI Tech Lead

My job is not limited to one technology.

I care more about solving the problem than choosing a particular tool.

I can work from the business requirement all the way to the production deployment.

That can include:

Understanding the problem

↓

Designing the solution

↓

Choosing the right technology

↓

Building the product

↓

Integrating AI where it makes sense

↓

Deploying it

↓

Working with the team

↓

Improving it based on real feedback

That end-to-end ownership is what I enjoy most.

What I'm Building and Exploring

Going forward, I'll be sharing more about the things I'm actually building and experimenting with.

Some of the areas I'm interested in are:

• AI agents for business workflows
• RAG systems
• AI-powered enterprise applications
• AI + Laravel
• AI + Node.js
• Local LLMs with Ollama
• AI SaaS architecture
• AI automation
• Production AI systems
• Building AI features into existing applications
• Lessons from leading engineering teams

I don't want this to be a place where I only share theoretical AI content.

I want to share what I build, what I learn from building it, what works, what doesn't, and why I made certain engineering decisions.

What's Next?

I've spent more than a decade building software.

I've worked as a developer, architect, and tech lead.

Now I'm combining that experience with AI to build systems that can solve more complex problems.

For me, the future of software engineering isn't about AI replacing engineers.

It's about engineers being able to solve problems that were previously too expensive, too slow, or too complex to solve.

And that's the direction I want to explore.

Build real things.

Solve real problems.

Use AI where it creates real value.

That's my journey toward becoming an AI-first Forward Deployed Engineer and this is where I'm starting to share it.

Thanks for reading. 👋

If you're also building AI-powered products or working on the intersection of AI and software engineering, I'd love to connect.