AI / Computer Vision Engineer — Complete Learning Curriculum
Target Role: AI / Computer Vision Engineer
Duration: 20 weeks (adjustable — work at your own pace)
Goal: Reach interview-ready expertise that places you in the top 1% of candidates
What You Will Build
By the end of this curriculum you will have:
- Classical CV expertise — OpenCV pipelines, feature engineering, camera geometry
- Deep learning fluency — PyTorch and TensorFlow, from tensors to custom training loops
- SOTA CV knowledge — YOLOv8, Mask R-CNN, Vision Transformers, CLIP, SAM, Diffusion
- Production engineering skills — ONNX, TensorRT, FastAPI, Docker, cloud deployment
- System design capability — scalable, GPU/TPU-accelerated inference architectures
- Interview readiness — 200+ coding problems, system design walkthroughs, concept cheatsheets
Folder Structure
cv-engineer/
├── README.md ← You are here
├── phase-00-foundations/ ← Python, NumPy, Math for ML
├── phase-01-classical-cv-opencv/ ← OpenCV, filtering, features, tracking
├── phase-02-ml-fundamentals/ ← sklearn, data pipelines, evaluation
├── phase-03-deep-learning-pytorch/ ← tensors, autograd, training, DataLoaders
├── phase-04-deep-learning-tensorflow/ ← Keras API, tf.data, custom training
├── phase-05-cv-deep-learning/ ← CNNs, transfer learning, detection, segmentation
├── phase-06-sota-architectures/ ← ViT, CLIP, SAM, Diffusion
├── phase-07-mlops-deployment/ ← ONNX, FastAPI, Docker, cloud, MLflow
├── phase-08-capstone-projects/ ← 3 end-to-end real-world projects
├── system-design/ ← Scalable CV systems, GPU/TPU, distributed training
└── interview-prep/ ← Concepts, coding problems, behavioral
20-Week Schedule
| Week | Phase | Focus |
|---|---|---|
| 1 | 0 | Python advanced patterns + NumPy for image ops |
| 2 | 0 | Math for ML: linear algebra, calculus, probability |
| 3 | 1 | Image basics, color spaces, histograms |
| 4 | 1 | Filtering, morphology, edge detection |
| 5 | 1 | Feature detection (Harris, SIFT, ORB), optical flow, tracking |
| 6 | 1–2 | Camera calibration + ML fundamentals kickoff |
| 7 | 2 | Data preprocessing, evaluation metrics (mAP, IoU, AUC) |
| 8 | 3 | PyTorch: tensors, autograd, building nn.Module |
| 9 | 3 | Training loops, loss functions, optimizers |
| 10 | 3–4 | DataLoaders + TensorFlow/Keras API |
| 11 | 4–5 | TF custom training + CNN fundamentals |
| 12 | 5 | Transfer learning: ResNet, EfficientNet, MobileNet |
| 13 | 5 | Object detection: YOLOv8 + Faster R-CNN |
| 14 | 5 | Semantic segmentation: U-Net, DeepLabV3+ |
| 15 | 5–6 | Instance segmentation + Vision Transformers |
| 16 | 6 | CLIP, SAM, Diffusion basics |
| 17 | 7 | MLOps: ONNX export, TensorRT, FastAPI inference |
| 18 | 7 | Docker, cloud (AWS/GCP), MLflow |
| 19 | 8 | Capstone Project 1 + 2 |
| 20 | 8 + Prep | Capstone Project 3 + full interview prep review |
Each Lab Structure
Every lab contains the following files:
| File | Purpose |
|---|---|
README.md | Deep theory, math derivations, algorithm internals, interview Q&A |
lab.py | Guided exercise with # TODO markers — fill in the blanks |
solution.py | Complete, production-quality solution with inline commentary |
exploration.ipynb | Jupyter notebook for visual/interactive exploration (select phases) |
requirements.txt | Pinned pip dependencies |
DATASETS.md | Download links and expected directory layout (where applicable) |
Prerequisites
- Python 3.10+ installed
- Basic Python familiarity (functions, classes, loops)
- A Linux/macOS environment or WSL2 on Windows
- GPU optional but recommended for phases 3–8 (CUDA 12+, at least 8 GB VRAM)
- Alternatively: Google Colab (free T4) or Kaggle Notebooks (free P100)
Hardware Recommendations
| Tier | Setup | Best For |
|---|---|---|
| Minimal | CPU-only laptop | Phases 0–2, small experiments |
| Mid | NVIDIA RTX 3060+ (8 GB) | Phases 3–6 comfortably |
| Recommended | NVIDIA RTX 4090 / A100 (24+ GB) | Phase 5+ with large batch sizes |
| Cloud | Google Colab Pro / Kaggle / Lambda Labs | On-demand GPU, no hardware cost |
| Enterprise | TPU v4 (GCP) / AWS Trainium | Distributed training at scale |
System Design Philosophy
Throughout this curriculum, every non-trivial solution is built with production scalability in mind:
- Throughput — how many images/second can this pipeline handle?
- Latency — what is the P99 inference time?
- Hardware efficiency — are we saturating GPU/TPU? What's the memory footprint?
- Fault tolerance — what happens if a model server crashes?
- Observability — how do we monitor model drift in production?
Each phase-7 and capstone lab explicitly addresses these dimensions.
Interview Strategy
The interview prep is organized as a running thread — not a last-minute cram. Each lab's README.md ends with interview questions and expected depth of answer. The interview-prep/ folder provides:
- Concept cheatsheets — one-page deep-dives with formulas
- ML/CV coding questions — implement from scratch with test cases
- System design — full walkthroughs for 5 common CV system design problems
- Behavioral — STAR-format answers for research presentation, cross-team collaboration
Tools & Technologies Covered
Languages: Python 3.10+, shell scripting
CV: OpenCV 4.x, Pillow, scikit-image
ML: scikit-learn, XGBoost
DL: PyTorch 2.x, TensorFlow 2.x / Keras, torchvision, timm
Detection: Ultralytics YOLOv8, Detectron2, torchvision (Faster R-CNN, Mask R-CNN)
Segmentation:U-Net, DeepLabV3+, SAM (Meta AI)
SOTA: Hugging Face Transformers, CLIP (OpenAI), Diffusers
Deployment: ONNX, TensorRT 8+, FastAPI, Triton Inference Server
Containers: Docker, docker-compose, NVIDIA Container Toolkit
Cloud: AWS SageMaker, GCP Vertex AI, S3 / GCS
Tracking: MLflow, Weights & Biases (W&B)
Augmentation:Albumentations, torchvision.transforms v2
Hardware: CUDA, cuDNN, NCCL (multi-GPU), Google TPU (JAX/XLA overview)
Quick Start
# 1. Clone / navigate to the curriculum root
cd /path/to/AI-Engineer/cv-engineer
# 2. Create a virtual environment (one per phase recommended)
python -m venv .venv && source .venv/bin/activate
# 3. Install phase-0 dependencies
pip install -r phase-00-foundations/lab-01-python-advanced/requirements.txt
# 4. Open the first lab
code phase-00-foundations/lab-01-python-advanced/lab.py
# 5. Run it
python phase-00-foundations/lab-01-python-advanced/lab.py
Mindset: This curriculum treats you as a practitioner, not a student. Every concept is paired with code that ships. Every design decision is explained. Every interview question is answered with the depth of someone who has built real systems.