Title: QA Test Engineer
Location: Alpharetta, GA
Duration: 7 months
Position type: W2 contract.
Face to Face interview is needed for this position.
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PS: Qualified QA Test Engineer candidates located near Alpharetta GA to be considered due to the position requiring an onsite presence.
Required Skills, Experience, & Abilities:
- 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. 
Additional skills required: 
- Hands-on with frameworks/tools such as: UI: Playwright / Cypress / Selenium and API: pytest + requests, Postman/Newman, REST Assured 
- 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.
Education: 
- Bachelor’s in Computer Science, Engineering, Data/Information Systems, or equivalent practical experience. 
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