Developer
AI Systems Architect
Master distributed AI systems using AI coding assistants daily.
Advanced Python
Async, decorators, metaclasses, memory optimization techniques.
Systems Design
Scalability, load balancing, microservices, failure modes.
ML Fundamentals
Supervised, unsupervised, reinforcement learning core principles.
Data Engineering
ETL pipelines, data warehousing, stream processing fundamentals.
Transformer Architecture
Attention mechanisms, positional encoding, multi-head attention.
LLM Training
Pretraining, fine-tuning, RLHF, instruction tuning methods.
Vector Embeddings
Semantic representations, similarity search, dimensionality reduction.
Distributed Training
Data parallelism, model parallelism, gradient synchronization techniques.
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