QA Test Engineer

QA Test Engineer

Location:

Alpharetta, GA, USA Duration: 12+ Month Contract

Must have strong AI experience

Education:

Bachelor's in Computer Science, Engineering, Data/Information Systems, or equivalent practical experience.

• 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/Type Script.
• 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
• CI/CD integration:
  Git, Git Hub Actions/Jenkins/Git Lab 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 Open Telemetry, 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.

• Familiarity with NLP concepts (embeddings, context windows, temperature/top-p).
• Experience with AI tooling:
  Lang Chain/Llama Index, evaluation tools, model gateways.
• Knowledge of regulatory/security needs relevant to the telecom domain.

• 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.