When I started my journey into AI engineering at 20 years old, I had no formal background and limited guidance. Through trial and error, I discovered that the initial 90 days are crucial for building momentum and avoiding common pitfalls. Today, I’m sharing the exact action plan I wish I’d had when beginning my journey – the same approach that helped me condense a decade-long career path into just four years.

Days 1-30: Building Your Foundation

The first month is about developing core knowledge that will support everything you do later. Many beginners make the mistake of immediately diving into advanced AI concepts without establishing fundamentals.

Start with these essential building blocks:

• Python proficiency: Focus specifically on data structures, functions, and working with external libraries – you don’t need to become a Python expert, just comfortable with the syntax and patterns used in AI implementations
• AI fundamentals: Understand tokens, embeddings, and vector representations – these are the building blocks of how AI models process and generate language
• System design basics: Learn how components of AI systems connect together – this big-picture view will help you see beyond individual models to complete solutions

Most importantly, focus on breadth over depth during this phase. Your goal isn’t mastery but developing the contextual understanding needed to absorb more complex concepts later.

Days 31-60: Applied Learning Through Projects

The second month transforms theoretical knowledge into practical experience. Contrary to popular advice, I recommend starting with implementation rather than theory.

Structure your applied learning around:

• Local AI model setup: Configure a basic local environment for running smaller AI models – this hands-on experience teaches you how these systems actually work
• Building a simple RAG system: Create a retrieval-augmented generation system using your own documents – this project touches on multiple essential AI engineering components
• API integration practice: Connect to existing AI cloud services to understand how production systems leverage these technologies

Each project should be documented in your portfolio, not just as completed work but as evidence of your problem-solving process. This portfolio becomes invaluable when demonstrating capabilities to potential employers.

Days 61-90: Specialization and Production Focus

The final month bridges the gap between creating AI systems and making them production-ready – the difference between hobbyists and professional engineers.

Focus your energy on:

• Optimization techniques: Learn approaches for making AI implementations more efficient and cost-effective
• Infrastructure fundamentals: Understand containerization and deployment strategies that scale AI applications
• Business value alignment: Develop the skill of connecting technical implementations to measurable business outcomes

This phase is where many aspiring AI engineers fall short – they build impressive prototypes but lack the knowledge to make them production-ready. By focusing on these areas, you position yourself as someone who delivers business value, not just technical experiments.

Accelerating Your Progress

Throughout this 90-day journey, I discovered several accelerators that dramatically increased my learning velocity:

Learning communities: Surrounding yourself with peers on similar journeys provides accountability and shortens your learning curve through shared experiences.

Applied focus: Reading and watching tutorials has limited value without implementation. Aim for a 20/80 split – 20% learning, 80% building.

Strategic project selection: Choose projects that demonstrate end-to-end capabilities rather than isolated technical tricks. Complete solutions impress employers more than clever code fragments.

The final crucial element is consistent action. Even on days when motivation is low, continuing forward progress – even small steps – maintains momentum and compounds your knowledge.

Beyond the First 90 Days

This plan isn’t about reaching the finish line of AI engineering knowledge – such a line doesn’t exist in a rapidly evolving field. Rather, these first 90 days build your learning foundation and implementation mindset.

The most valuable outcome isn’t just technical knowledge but developing the ability to continuously adapt and implement new AI technologies as they emerge. This adaptability becomes your career superpower in a field where change is the only constant.

Ready to put these concepts into action? The implementation details and technical walkthrough are available exclusively to our community members. Join the AI Engineering community to access step-by-step tutorials, expert guidance, and connect with fellow practitioners who are building real-world applications with these technologies.