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Über
Duration: 9 Months+ (Good possibility of extension)
Location: Alpharetta, GA
Job Description:
Education / Background: 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.
Core QA Automation Skills - Strong test automation in Python and/or Java/TypeScript.
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 would be plus not mandatory)
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.
Sprachkenntnisse
- English
Hinweis für Nutzer
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