QA Test EngineerLocation: Alpharetta, GA, USA Duration: 12+ Month ContractMust have strong AI experience Education: Bachelor's in Computer Science, Engineering, Data/Information Systems, or equivalent practical experience.Top 5 Skills Required:3+ years in QA automation or SDET-type work (adjust by level); 1+ year exposure to AI/LLM or ML-driven features is a plus.Strong test automation in Python and/or Java/TypeScript.We are a platform team, testing APIs for high performance, automation will be primary focus.Strong communication and analytical skills.Hands-on with frameworks/tools such as: UI: Playwright / Cypress / Selenium and API: pytest + requests, Postman/Newman, REST Assured.Additional Skills Required:CI/CD integration: Git, GitHub Actions/Jenkins/GitLab CI, test reporting, gating.Test design: equivalence partitioning, boundary testing, risk-based testing, defect triage.AI-Specific Testing Competencies (Key):LLM/application behavior testing: validating correctness when outputs are probabilistic.Evaluation strategies: golden datasets, scoring rubrics, human-in-the-loop reviews.Non-determinism handling: statistical assertions, repeated runs, variance thresholds.Prompt and regression management: versioning prompts, detecting prompt drift, replay tests.RAG testing (if applicable): retrieval quality (recall/precision), grounding checks, citation validation, doc freshness.Safety & quality checks: hallucination detection, toxicity/PII leakage checks, policy compliance tests.Data & Observability: Ability to create and maintain test datasets (structured + unstructured), including edge cases.Familiarity with telemetry for AI systems: logging prompts/outputs safely, traceability, correlation IDs - tools like OpenTelemetry, ELK/Splunk, Datadog/Grafana (any equivalent).Understanding of data privacy constraints (masking/redaction) and secure test data practices.API / Microservices / Cloud: Comfortable testing distributed systems: microservices, async workflows, queues/events.Basic cloud proficiency (AWS/Azure/GCP) and containerization (Docker, optional Kubernetes).Performance & Reliability Testing (AI-Aware): Load/performance testing for inference endpoints (latency, throughput, concurrency).Cost-aware testing (token usage, rate limits, fallbacks).Resilience tests: retries, circuit breakers, model timeouts, degraded-mode behavior.Nice-to-Have Domain Knowledge: Familiarity with NLP concepts (embeddings, context windows, temperature/top-p).Experience with AI tooling: LangChain/LlamaIndex, evaluation tools, model gateways.Knowledge of regulatory/security needs relevant to the telecom domain.Soft Skills / Ways of Working:Strong communication —able to explain AI quality issues clearly to product and engineering.Comfortable partnering with data science/ML engineers and backend teams.Ownership mindset: building reusable test harnesses, improving quality metrics, preventing regressions.