AI Roadmap for Tech Leads. An 18-Month Learning Plan to Stay Relevant and Lead with Intelligence
Beyond Code: My 18-Month AI Roadmap as a Tech Lead
For the past 10+ years, I’m’ leading mobile engineering teams, shipped apps across platforms, and helped others grow into better developers.
But I’ve always believed that strong leadership means never becoming static.
That’s why, not long ago, I started building something for myself:
A long-term, flexible learning path to deepen my understanding of AI and Machine Learning, not to “switch careers,” but to enhance how I think, lead, and build as a technical leader.
Why AI/ML Matters for Tech Leads Today
We’re not in the AI hype era anymore—AI is infrastructure.
Whether it’s OCR in mobile apps, recommendation engines, or fraud detection, it’s reshaping how we build products and lead teams.
If you’re a tech lead or senior engineer, the real shift is this:
You don’t need to become a data scientist.
But you do need to understand how data, models, and intelligent systems are shaping modern development.
AI is now part of the tech lead toolbox—like architecture, code review, or system design.
My 18-Month AI Learning Roadmap (Flexible and Evolving)
This roadmap isn’t rigid. It evolves based on:
- What I’m curious about
- How the field progresses
- What’s most useful to my current and future work
Months 1–3 | Technical Foundations
| Month | Course / Certification | Link | Hours | Goal |
|---|---|---|---|---|
| 1 | Python for Everybody | Python | ~20 | Apply AI techniques to specific chemistry problems |
| 1-2 | Mathematics for Machine Learning (Imperial College) | Math for ML | ~60 | Linear algebra, calculus, PCA applied to ML |
| 2–3 | Machine Learning Specialization (DeepLearning.AI) | Machine Learning Specialization | ~60 | Master core ML concepts: regression, classification, clustering |
Project: Develop a mobile AI app for experimental detection and recommendations in chemistry. Document progress on GitHub.
Months 4–6 | Architecture, MLOps, and Scalability
| Month | Course / Certification | Link | Hours | Goal |
|---|---|---|---|---|
| 4–5 | Google Cloud Machine Learning Engineer | GCP ML Engineer | ~60-80 | Build ML pipelines, deploy models with Vertex AI |
| 5–6 | MLOps Specialization | MLOps | ~30-40 | Learn CI/CD, model monitoring, versioning |
Project: Build an end-to-end ML pipeline for experimental data — from training to cloud deployment.
Months 7–9 | Product Management, Consulting, and Industry Impact
| Month | Course / Certification | Link | Hours | Goal |
|---|---|---|---|---|
| 7–8 | AI Product Management Specialization (Duke) | AI Product Management | ~30 | Product lifecycle, ethics, and AI use cases |
| 8–9 | AI Innovation & Product Strategy Specialization | Product Strategy | ~40 | Use AI to Strategize, Design, Automate, and Deliver Sustainable Innovation |
Activity: Publish blog posts on AI applications.
Months 10–12 | Deep Learning and Generative AI Applications
| Month | Course / Certification | Link | Hours | Goal |
|---|---|---|---|---|
| 10–11 | Deep Learning Specialization (DeepLearning.AI) | Deep Learning Specialization | ~50 | CNNs, RNNs, Transformers, Autoencoders |
| 11–12 | Introduction to Generative AI (Google Cloud) | Generative AI Specialization | ~30 | Large Language Models, ethics, and generative AI applications |
Project: Develop generative models for molecular synthesis and synthetic data generation in chemistry.
Months 13–15 | Advanced AI Strategy and Digital Transformation
| Month | Course / Certification | Link | Hours | Goal |
|---|---|---|---|---|
| 13–14 | Digital Transformation Using AI/ML (Google Cloud) | Digital Transformation | ~30-40 | Apply AI in business processes |
| 14–15 | IBM AI Product Manager Professional Certificate | IBM AI PM | ~40 | Manage AI product lifecycle, prompt engineering |
Project: Lead an AI transformation project internally or as a consultant.
Months 16–18 | Scalable Architecture and Advanced Certifications
| Month | Course / Certification | Link | Hours | Goal |
|---|---|---|---|---|
| 16–17 | Google Cloud Professional Machine Learning Engineer Prep | ~40 | Prepare for Google Cloud certification | |
| 17–18 | Optional: AWS Certified Machine Learning Specialty | AWS ML Specialty | ~40 | Multi-cloud advanced ML certification |
It’s Not a Rigid Plan—And That’s the Point
Learning isn’t linear.
Some weeks I go deeper into TensorFlow, other weeks I just read papers or run image detection tests with my own data.
This roadmap is living. And it should be. This roadmap isn’t carved in stone — courses or tools may be replaced along the way as technologies evolve and my needs as a tech lead shift.
Because real growth happens when you build, reflect, share… and iterate.
Final Thoughts for Other Tech Leads
- You don’t need to reinvent yourself.
- You just need to stay relevant, curious, and adaptive.
- A little structure beats pure curiosity.
- A little curiosity beats pure planning.
This is the long game. And if you’re on a similar journey—whether it’s AI, Web3, or quantum—I’d love to follow along.
Feel free to share your roadmap or thoughts.
Let’s lead with code and with insight.