AI Engineering Track
Build the Future of Intelligence
Go beyond prompt engineering. Master the architectural foundations of Transformers, architect production RAG systems, and scale machine learning infrastructure.
Engineering Specialties
01
Deep Learning Foundations
Cinematic Lecture
02
PyTorch: CNN from Scratch
GPU Sandbox
03
MLOps: Kubeflow Pipelines
GPU Sandbox
04
Feature Engineering: Feast & Spark
GPU Sandbox
05
Distributed Training: Ray
GPU Sandbox
06
Model Quantization: ONNX
GPU Sandbox
07
Reinforcement Learning: RLlib
GPU Sandbox
08
Time Series: Prophet & LSTM
GPU Sandbox
09
Object Detection: YOLOv8
GPU Sandbox
010
NLP: BERT Fine-Tuning
GPU Sandbox
Production-Ready Competencies
Transformers ArchitectureDistributed TrainingVector DatabasesModel QuantizationAgentic WorkflowsMLOps Pipelines
Capstone 01
ProjectDistributed Real-Time Fraud Engine
Architect a high-throughput fraud detection system using Spark for stream processing and Ray for distributed model training.
Distributed Training
Stream Processing
Capstone 02
ProjectAutonomous Trading Agent
Develop a reinforcement learning agent using RLlib to optimize trading strategies in a simulated high-frequency market.
Reinforcement Learning
RLlib
Capstone 03
ProjectEdge AI Deployment Pipeline
Quantize and optimize a complex computer vision model using ONNX and TensorRT for real-time inference on edge devices.
Model Quantization
Edge Inference
Resume Integration
Your engineering work is automatically translated into high-impact bullet points for your technical resume.
Build Your Resume