Python for AI Engineers: Async Services, Type Safety, and Production Patterns

Python for AI engineering interviews focus on building reliable services around non-deterministic models. Interviewers ask how you structure FastAPI apps, manage async calls to multiple LLM and retrieval providers, and enforce timeouts that prevent hung requests from exhausting workers.

Pydantic models and strict typing reduce runtime surprises when parsing tool outputs and API responses. Candidates discuss validation errors, retry policies for transient provider failures, and structured logging that preserves trace context across await boundaries.

Concurrency patterns matter for batch embedding and evaluation jobs. Explain asyncio versus thread pools versus process pools for CPU-bound preprocessing. Memory spikes from loading large documents or model artifacts are common production incidents—address streaming and chunk-wise processing.

Testing AI applications requires contract tests, recorded fixtures, and eval harnesses—not deterministic unit tests alone. Interviewers appreciate discussion of VCR-style provider mocks, property-based tests for parsers, and integration tests that run against staging indexes.