AI & Machine Learning Engineering

Design, train, optimize, deploy, and monitor production machine learning models and intelligent agent systems with rigorous validation, latency engineering, and ethical guardrails.

Core Principle

Every ML solution must beat a naive baseline, guarantee train-test isolation, and meet strict production latency and fairness budgets.

Core Invariants

1. Strict Train-Test Isolation: Preprocessing scalers, encoders, and tokenizers must be fit strictly on training folds inside isolated pipelines to eliminate data leakage.
2. Sub-100ms Inference Budget: Synchronous user-facing endpoints must achieve $p99 < 80\text{ms}$ through quantization (ONNX/TensorRT/GGUF), engine optimization, or continuous batching.
3. Mandatory Baseline Before Complexity: Always benchmark against a naive baseline (majority class/mean) and simple linear model before advancing to deep networks or complex ensembles.
4. Demographic Parity & Bias Auditing: Every production candidate must pass four-fifths disparate impact testing across demographic slices ($\text{ratio} \ge 0.80$).
5. Continuous Drift Observability: Production models must emit data drift metrics (KS-test / Population Stability Index) and concept drift alerts to trigger automated retraining.