Test Strategy Planning
Define QA strategy for AI/ML services, microservices, and APIs across environments.
Translate requirements and ML acceptance criteria into test plans and traceable test cases.
Establish performance baselines and SLOs for models and services.
Test Design Execution
Unit testing (PyTest/unittest) for data transformations, feature engineering utilities, and model wrappers.
Integration testing for pipelines (data ingestion training validation deployment) and microservices.
API testing (REST/GraphQL) for inference endpoints, auth flows, and rate limits.
Performance load testing to validate latency, throughput, and p95/p99 targets.
Build automated test suites in Python and integrate them into CI/CD.
AI/ML Quality Validation
Validate model outputs against ground truth, statistical thresholds, and confidence scoring rules.
Test for model regression across versions; maintain golden datasets and baseline metrics (accuracy, F1, ROC-AUC, precision/recall, calibration).
Perform data quality checks (schema, drift, leakage) and monitor post-deployment drift.
Verify explainability artifacts SHAP/feature importance) where applicable.
Observability Defect Management
Instrument tests with logs/metrics/traces; create dashboards for quality gates.
Triage defects, conduct root cause analysis, and drive closure.
Required Skills Qualifications
Python proficiency for test automation and data validation (PyTest/unittest, requests/httpx).
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