For an AI Engineer / AI Solutions Architect role, the gap is mainly: ML fundamentals → modern LLMs → AI application architecture → production deployment.

1. What companies will actually expect

There are roughly four layers:

                 AI SOLUTIONS ARCHITECT
                         ↑
              Architecture & Cloud
                         ↑
             AI Applications / LLMs
                         ↑
              ML & Deep Learning
                         ↑
             Python + Software Engineering

2. First: understand ML properly

Start with

data → model → training → weights → prediction

is exactly where I would start.

Learn:

• Linear regression
• Classification
• Training / validation / test data
• Loss functions
• Gradient descent
• Overfitting
• Feature engineering
• Neural networks
• Embeddings
• Transformers
• Attention

You don't need to become a mathematician.

You need enough mathematics to understand what the algorithms are actually doing.

3. Then learn Deep Learning

Use PyTorch as your main framework.

You should be able to build something like:

Dataset
   ↓
PyTorch model
   ↓
Training
   ↓
Validation
   ↓
Save model
   ↓
Load model
   ↓
Prediction

You should understand what happens inside rather than simply calling an API.

4. Then move heavily into LLMs

This is probably the most important area for the current AI-engineering market.

LLM fundamentals

• Tokens
• Tokenization
• Embeddings
• Vector representations
• Transformers
• Attention
• Context window
• Positional information
• Pre-training
• Fine-tuning
• Instruction tuning
• RLHF / preference tuning
• Inference

You are already asking exactly the right questions about weights, vectors and training.

5. Then learn how companies actually build AI applications

This is where AI Engineer becomes different from someone who has simply studied ML.

For example, a company may say:

"We have 50,000 engineering documents. Build an AI assistant that can answer questions about them."

You might design:

                    User
                      ↓
                 Web / API
                      ↓
                 LLM application
                      ↓
              Retrieval / RAG
                      ↓
              Vector database
                      ↓
             Engineering documents

You need to understand:

• RAG
• Chunking
• Embeddings
• Vector databases
• Metadata filtering
• Retrieval
• Reranking
• Prompt design
• Tool/function calling
• Structured output
• Agents
• Evaluation
• Guardrails
• Hallucination reduction