Position Description:
Position Title - QA Test Engineer
Job Location - Alpharetta, GA, USA
Bill Rate Range - $58– $60/hr
Estimated Duration (In Months) - 7
Work Model - Onsite
Must have Skills/Attributes - API, Automation, CI/CD tools, GIT, Java, LLM (Large Language Model), Python, QA
Shift - Standard Business hours
Must 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.
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/Google Cloud Platform) 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.
Never miss an opportunity! Create an alert based on the job you applied for.