About
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. 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/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.
Languages
- English
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