I see too many early-stage deep-tech teams chasing unicorns because they haven't defined what the role actually needs.
I use a framework called 3-3-3:
• 3 skills they must have day one
• 3 they can learn in the first 3 months
• 3 they'll probably never need to do
The power is in the last column. It forces you to cross things off and stop filtering for irrelevant signals.
Every time I run this exercise with a founding team, the job description shrinks by 40% and the candidate quality goes up. By the way, this doesn't ONLY work for tech teams haha! If you're hiring right now, try it this week.
Built SkillMap AI — an AI-powered career roadmap platform that turns a user’s career goal into a personalized learning path with skills, projects, and resources. Built with Next.js, React, Tailwind CSS, FastAPI, PostgreSQL, and LLM integration.
Job searching can be overwhelming when you have to create your CV, write applications, prepare for interviews, track applications, and figure out what to say to recruiters—all from scratch.
So I built a Customer Service Career Kit: a practical digital toolkit designed to help customer service and support professionals approach their job search with more structure and confidence.
I designed it to be practical, reusable, and easy to customize—not just another collection of generic templates.
This is my first step into building digital products around real career and customer-support challenges, and I'm excited to keep developing it.
If you're a customer service professional, job seeker, or someone building a career in customer support, I'd love to hear what resource you think should be added next.
🚀 Turning AI & ML Projects into Real, Usable Applications 🤖💜
From building a Streamlit AI-powered application to developing a Customer Churn Prediction model, every project teaches me something new — not just about technology, but about solving real-world problems with AI & Machine Learning. ✨
🔹 Streamlit App — turning ML ideas into interactive and user-friendly web applications
🔹 Customer Churn Predictor — using Machine Learning to predict whether a customer is likely to churn or stay, along with churn probability and customer risk level 📊🚦
🔹 Business Insights — providing actionable recommendations that can help businesses focus on customer retention 💡
🔹 End-to-End Project — from data preprocessing and model training to testing, building and deploying a working ML application 🌐
This project is designed to show how a Machine Learning model can move beyond a notebook and become a real, usable application that can help businesses understand customer churn risk and make better decisions. 🤝
This is more than just a project showcase — it’s a reminder that every line of code brings me one step closer to becoming the AI/ML engineer I aspire to be. ✨
Still learning.
Still building.
Still improving. 🚀