SachinDas246

What is AiWhiz?

AiWhiz started as a group of data scientists with over 20 years of experience in data science and machine learning. They came together with a vision to build an AutoML platform—a tool that automates the entire machine learning process, from data collection to model deployment. The idea was to simplify machine learning for businesses that don’t have a dedicated data science team.

I joined the core team of AiWhiz right after completing my BTech. Being a part of a small, tight-knit team meant I got to dip my toes in almost everything—development, client interactions, even some marketing. It was a wild, rewarding ride! Here's how it went:

Note: I have refrained from disclosing the exact and detailed information of the job to protect the private information of the company. I’ve only shared details that are relevant to my experience and that I was allowed to disclose.

The Early Days

In the beginning, I worked mainly as a Python web developer. I was responsible for setting up the core backend using Django, designing the database, and building the initial views for our MVP. This early version allowed users to upload data and train models.

It was exciting to see an idea turn into something real, even if it was just a basic version.

Going Full Stack

Once we had a working backend, I started taking on full stack responsibilities. This included:

• User registration and login features
• Email verification
• Subscription system to manage user access
• Payment gateway integration (we used Paytm)

This phase really helped me bridge the gap between backend and frontend development. It was my first real experience creating a user-facing product end-to-end.

Model Engine Refactoring

As our platform grew, things started to slow down—literally. The core model engine, originally built in pure Python, just couldn’t keep up with the increasing number of models and users.

I was given two weeks to refactor the entire core model engine. It was intense, but thrilling. During this time, I:

• Worked closely with senior developers to understand the existing (and super complex) codebase.
• Cleaned up and optimized the code for better performance.
• Made sure that the refactoring didn't break any existing functionality.

Those two weeks taught me a lot about code optimization, reading legacy code, and collaborating effectively with senior engineers.

Modularizing into Different Containers

After successfully refactoring the core model engine, we realized that boosting performance wasn’t enough—we needed a scalable structure.

So, we moved forward by modularizing the application into different containers. This phase involved:

• Breaking down functionalities into separate, manageable modules.
• Dockerizing the application for easier and consistent deployments.
• Introducing Celery to handle heavy data processing asynchronously.

This approach made the platform much more scalable, maintainable, and future-proof.

Implementing CI/CD Pipeline

With the platform becoming more complex, manual testing and deployment became a huge time sink. So, I set up a custom CI/CD pipeline using GitLab that:

• Automatically ran tests on new commits.
• Deployed the updates directly to our servers.

It made a huge difference in speeding up our development cycles.

Life at AiWhiz Beyond Code

AiWhiz was my first full-time job, and it changed a lot for me. When I joined, I was just a self-taught web developer. Meeting Smithish Jose, the founder, and hearing his vision of bringing AI to small and medium businesses (way before ChatGPT and LLMs were trending!) was genuinely inspiring.

Working here didn’t just level up my coding skills—I learned how to:

• Talk to clients and understand their pain points.
• Market and position a tech product.
• See the bigger picture of how startups operate.

This experience shifted my perspective on what it means to be a tech professional. It’s not just about writing code; it’s about building something meaningful that solves real problems.

Final Thoughts

Looking back, joining AiWhiz was one of the best decisions I’ve made. It set the foundation for my career and gave me a front-row seat to what it takes to build a product from the ground up. From writing Django models to deploying with CI/CD, and from coding to client calls—I got to wear many hats, and I loved every bit of it.

If you’re reading this and just starting out, don’t be afraid to dive in headfirst. You never know where that first job or project might take you!