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Read More →Download the AI engineer skills checklist PDF from Uncodemy: programming, maths, ML, deep learning, LLM and MLOps skills with levels.
This page offers Uncodemy's AI engineer skills checklist as a PDF download. It lists the skills an AI engineer is expected to build, grouped by area and by level, so you can mark what you already know and see what is missing. Use it to plan study, review a portfolio or prepare for interviews.
Three quick-access entry points into the same AI engineer skills checklist PDF: the full guide, the programming + data + maths checklist tables pack, and the deep learning + LLM + production reference. All PDFs on this page are free to download. No form is required.
The complete skills checklist — what an AI engineer does, skill areas at a glance, soft skills, common mistakes and how to use the checklist.
The core foundation checklists: Python, Git, SQL, pandas and NumPy, testing, linear algebra, probability, calculus, supervised and unsupervised learning.
Deep learning and LLM checklists (neural networks, transformers, prompting, RAG, agents, LLM evaluation, vector search) plus production and responsibility reference.
All three download buttons open the same AI engineer skills checklist PDF file. By downloading, you agree to our Privacy Policy. These PDFs are study material only — not a certificate or job guarantee.
The title AI engineer is used differently by different employers, so the checklist starts with a working definition. An AI engineer builds software systems that use machine learning models: collecting and preparing data, training or adapting models, evaluating them, wrapping them in services and keeping them working in production. In recent years many roles focus on applications built on pretrained large language models, where the work is prompt design, retrieval, evaluation and integration rather than training from scratch. Other roles lean towards classical machine learning, computer vision or MLOps. Read job descriptions carefully and tick the skills that match the roles you want, rather than trying to master every box at once.
| Area | Core idea | Why it matters |
|---|---|---|
| Programming | Python, version control, testing | Every other skill is applied through code |
| Maths and statistics | Linear algebra, probability, statistics, calculus basics | Needed to understand models and evaluation |
| Data skills | SQL, pandas, cleaning, feature work | Models are only as good as their data |
| Machine learning | Supervised and unsupervised learning, evaluation | The foundation under most AI systems |
| Deep learning | Neural networks, frameworks, transformers | Behind vision, speech and language models |
| LLM applications | Prompting, retrieval, agents, evaluation | Most current AI engineer job openings |
| MLOps and deployment | APIs, containers, monitoring, pipelines | Moves models from notebook to production |
| Responsible AI | Bias, privacy, safety, transparency | Reduces harm and business risk |
| Communication | Explaining trade-offs, documenting, collaborating | AI work is a team activity |
| Skill | Beginner | Working level | Strong |
|---|---|---|---|
| Python | Syntax, functions, collections | Classes, modules, virtual environments, packaging | Typing, async, performance profiling |
| Git | Commit, push, pull | Branches, pull requests, resolving conflicts | Release workflows and code review habits |
| SQL | SELECT, WHERE, GROUP BY | Joins, CTEs, window functions | Query tuning and data modelling |
| pandas and NumPy | Load, filter, summarise | Merge, reshape, vectorised operations | Memory and speed optimisation |
| Testing | Run basic tests | Write unit tests with pytest | Test data and model behaviour |
| Skill | Beginner | Working level | Strong |
|---|---|---|---|
| Linear algebra | Vectors and matrices | Dot product, matrix multiplication, dimensionality ideas | Decompositions and their uses |
| Probability and statistics | Mean, variance, distributions | Hypothesis tests, confidence intervals, Bayes rule | Experiment design and causal thinking |
| Calculus | Derivative meaning | Gradients and the chain rule | Optimisation behaviour |
| Supervised learning | Regression and classification basics | Trees, ensembles, regularisation, cross-validation | Calibration, imbalance, feature engineering |
| Evaluation | Accuracy | Precision, recall, F1, ROC AUC, error analysis | Metric choice tied to business cost |
| Unsupervised learning | Clustering idea | K-means, PCA | Evaluation without labels |
| Skill | Working-level indicator |
|---|---|
| Neural networks | Can explain layers, activation, loss and backpropagation in plain words |
| A deep learning framework | Can train and evaluate a small model with PyTorch or TensorFlow |
| Transformers | Can describe attention and tokenisation at a high level |
| Prompt design | Writes clear instructions, examples and output formats and tests them |
| Retrieval-augmented generation | Can chunk documents, embed, retrieve and cite sources |
| Agents and tools | Can let a model call functions with validation and limits |
| LLM evaluation | Has a test set and measures quality, cost and latency |
| Vector search | Understands embeddings, similarity and index trade-offs |
Ready to build a portfolio that proves these skills to employers? Explore Uncodemy's Generative AI / Agentic AI course or book a free demo and learn from industry trainers.
