JOB DESCRIPTION:
Agentic QA Engineer – Generative AI & Agentic Systems (Agent, MultiAgent Testing)
Location: Dallas, TX
Rate: $60/hr on Corp to Corp
Duration: Long term
Mode of interview: One Virtual and One face to face
Mode of Job: 5 Days onsite
Summary
We are seeking a hands-on AI Engineer to design and execute end-to-end testing strategies
for agentic AI solutions, including multi-agent systems in production-grade environments.
This role partners with the Agentic Operations Team to ensure resiliency, reliability,
accuracy, latency, orchestration correctness, and scale. You will establish QA
frameworks, build reusable test artifacts, drive macro-level validations across complex
workflows, and lead the QA function for Agentic AI from Dev to Prod.
Key Responsibilities
Agentic & MultiAgent Testing
Reliability, Resiliency, and Latency
Accuracy & Macro-Level Validations
Scale & Orchestration
Dev → Prod Readiness
Define and own the QA strategy for agentic/multi-agent AI systems across dev,
staging, and prod.
Mentor a team of QA engineers; establish testing standards, coding guidelines for
test harnesses, and review practices.
Partner with Agentic Operations, Data Science, MLOps, and Platform teams to
embed QA in the SDLC and incident response.
Design tests for agent orchestration, tool calling, planner-executor loops, and
inter-agent coordination (e.g., task decomposition, handoff integrity, and
convergence to goals).
Validate state management, context windows, memory/knowledge stores, and
prompt/graph correctness under varying conditions.
Implement scenario fuzzing (e.g., adversarial inputs, prompt perturbations, tool
latency spikes, degraded APIs).
Create resilience testing suites: chaos experiments, failover, retries/backoff, circuit-
breaking, and degraded mode behavior.
Establish latency SLOs and measure end-to-end response times across
orchestration layers (LLM calls, tool invocations, queues).
Ensure reliability through soak tests, canary verifications, and automated rollbacks.
Define ground-truth and reference pipelines for task accuracy (exact match,
semantic similarity, factuality checks).
Build macro validation frameworks that validate task outcomes across multi-step
agent workflows (e.g., complex data pipelines, content generation + verification agent
loops).
Instrument guardrail validations (toxicity, PII, hallucination, policy compliance).
Design load/stress tests for multi-agent graphs under scale (concurrency,
throughput, queue depth, backpressure).
Validate orchestrator correctness (DAG execution, retries, branching, timeouts,
compensation paths).
Engineer reusable test artifacts (scenario configs, synthetic datasets, prompt
libraries, agent graph fixtures, simulators).
Integrate tests into CI/CD (pre-merge gates, nightly, canary) and production
monitoring with alerting tied to KPIs.
Define release criteria and run operational readiness (performance, security,
compliance, cost/latency budgets).
Required Qualifications
7+ years in Software QA/Testing, with 2+ years in AI/ML or LLM-based systems;
hands-on experience testing agentic/multi-agent architectures.
Strong programming skills in Python experience building test harnesses, simulators,
and fixtures.
Experience with LLM evaluation (exact/soft match, BLEU/ROUGE, BERTScore,
semantic similarity via embeddings), guardrails, and prompt testing.
Expertise in distributed systems testing latency profiling, resiliency patterns (circuit
breakers, retries), chaos engineering, and message queues.
Familiarity with orchestration frameworks (LangChain, LangGraph, LlamaIndex,
DSPy, OpenAI Assistants/Actions, Azure OpenAI orchestration, or similar).
Proficiency with CI/CD (GitHub Actions/Azure DevOps),
observability (OpenTelemetry, PrometheGrafana, Datadog), and feature
flags/canaries.
Solid understanding of privacy/security/compliance in AI systems (PII handling,
content policies, model safety).
Excellent communication and leadership skills; proven ability to work cross-
functionally with Ops, Data, and Engineering.
Preferred Qualifications
Experience with multi-agent simulators, agent graph testing, and tooling latency
emulation.
Knowledge of MLOps (model versioning, datasets, evaluation pipelines) and A/B
experimentation for LLMs.
Background in cloud (AWS), serverless, containerization, and event-
driven architectures.
Prior ownership of cost/latency/SLAs for AI workloads in production.
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