Sr. QA Test Engineer

Overview

Title: QA Test Engineer

Location:

Alpharetta, GA
Duration: 7 months
Position type: W2 contract.

Face to Face interview is needed for this position.

• Perform basic cloud proficiency (AWS/Azure/GCP) and containerization (Docker, optional Kubernetes). Include 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.

• 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: UI:
  Playwright / Cypress / Selenium; 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.

• 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 (or equivalents).
• 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.