ai-ml-engineer

Compare prompting, retrieval, tool use, and fine-tuning based on evidence from a baseline experiment. Start with the simplest approach, evaluate on representative cases, and only add fine-tuning or retrieval complexity when measurements justify it.

Evaluate on representative and adversarial cases rather than average accuracy alone. Establish a trustworthy dataset split with leakage checks, build a baseline first, and produce an evaluation report with error analysis and calibration.

Monitor quality drift, data drift, latency, cost, and safety after deployment. Package inference behind a stable interface with timeouts, fallbacks, and observability so regressions are detected early.

Avoid adding complexity without a measured requirement. Build a baseline first, prefer the simplest option that satisfies the requirement, and never optimize without measurement or claim production readiness without evidence.

Track experiments, model versions, data versions, and configuration throughout the lifecycle. Use data/version management practices so results can be reproduced and remaining limitations are clearly documented.