AI Engineer Portal
Your personal operating system for career transition.
Python for AI Engineers
Build practical Python fluency for APIs, pipelines, evaluation scripts, and debugging in applied AI systems.
Python runtime habits that matter in AI work
Data modeling with Pydantic, boundary layers for API responses, idempotent scripts, and logging patterns for AI debugging.
Python runtime habits that matter in AI work
Data modeling with Pydantic, boundary layers for API responses, idempotent scripts, and logging patterns for AI debugging.
Async patterns for AI workloads
asyncio for concurrent LLM calls, gathering multiple provider requests, rate limiting with semaphores, and connection pooling.
Data pipelines for AI ingestion
Processing documents for ingestion, streaming data transformations, generators for memory efficiency, and batch vs stream processing tradeoffs.
Type safety and validation patterns for AI systems
Advanced Pydantic patterns, discriminated unions for provider responses, generic types for AI abstractions, and runtime validation strategies.
Performance and profiling for AI pipelines
Profiling LLM-heavy code, caching strategies with TTL, memory management with large contexts, and concurrent processing patterns.
Pydantic for AI applications
Structured output validation from LLMs, settings management with BaseSettings, schema generation for tool definitions, and response models for provider APIs.
Async concurrency for AI workloads
asyncio.gather for parallel provider calls, semaphores for rate limiting concurrent requests, streaming response handling with async generators, and timeout patterns.
Testing AI applications
Why AI testing is different from conventional testing, mocking LLM providers for unit tests, snapshot testing for prompt outputs, and eval-driven testing with golden datasets.
CLI tools and scripts for AI workflows
Building eval runners with typer, batch processing scripts with progress tracking, data preparation pipelines, and machine-readable report output.
Error handling for unreliable AI services
Retry strategies with exponential backoff and jitter, circuit breaker pattern for provider failures, timeout cascading in multi-step pipelines, and partial failure handling.