AI Engineer: Skills, Projects & Interview Questions (2026)

Beginner · 3-5 months

Foundations & core tooling

Build: Build a sentiment classifier in Python + scikit-learn and expose it as a simple script.

Intermediate · 5-6 months

Applied, real-world builds

Build: Ship a RAG chatbot over your own docs using an LLM API, embeddings and a vector DB.

Advanced · 6-8 months

Production, scale & specialization

Build: Deploy a fine-tuned/served LLM feature with evaluation, guardrails and basic MLOps monitoring.

Train a text classifier and serve predictions via a REST endpoint.

Skills: Python, ML, scikit-learn, REST

Build and deploy a CNN image classifier with a simple UI.

Skills: Python, Deep Learning, PyTorch

Answer questions over uploaded resumes using embeddings + an LLM.

Skills: RAG, Embeddings, Vector DB, LLM API

Summarize long PDFs with an LLM and chunking pipeline.

Skills: LLMs, Prompt Engineering, Python

Vector search over a corpus with reranking.

Skills: Embeddings, Vector DB, RAG

LLM tool that reviews pull requests and suggests fixes.

Skills: LLMs, Prompt Engineering, APIs

Content/collaborative recommender with an API.

Skills: ML, Python, Model Deployment

Fine-tune (LoRA) a model for a niche domain with evaluation.

Skills: Fine-tuning, LoRA, Evaluation

Generate captions from images using a vision-language model.

Skills: Deep Learning, Transformers, PyTorch

Deploy an LLM feature with guardrails, caching and monitoring.

Skills: MLOps, LLM API, Guardrails