Sr. QA Test Engineer

Accord Technologies Inc; Alpharetta (GA); On-site; USD 80,000 - 100,000; Full time

Job summary

Accord Technologies Inc is seeking a QA Test Engineer for a 7-month W2 contract in Alpharetta, GA. The role involves performance testing, automation, and ensuring AI system quality.

The ideal candidate has over 3 years in QA automation, strong skills in Python or Java/TypeScript, and a background in testing APIs. Familiarity with cloud platforms is important. Strong communication skills are essential for collaborating with product and engineering teams.

Qualifications

• 3+ years in QA automation or SDET work.
• Exposure to AI/LLM or ML-based features is a plus.
• Strong communication and analytical skills.

Responsibilities

• Perform cloud proficiency and containerization.
• Conduct load/performance testing for inference endpoints.
• Implement resilience tests for ML models.

Skills

QA automation

Python

Java/TypeScript

API testing

Strong communication skills

Tools

Playwright

Cypress

Selenium

Git

Jenkins

Postman

Datadog

Job description

Overview

Title: QA Test Engineer
Location: Alpharetta, GA
Duration: 7 months
Position type: W2 contract.

Face to Face interview is needed for this position.

Responsibilities

• 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.

Top 5 Skills / Qualifications

• 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

• Hands‑on with frameworks/tools: UI: Playwright / Cypress / Selenium; 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 (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.

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.