AI QA ENGINEER (AGENTIC & GENERATIVE)

• Quality Strategy & Leadership
• Agentic & Multi Agent 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).
• Build post-deployment validation playbooks and incident triage runbooks.

• 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 or TypeScript/JavaScript; 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, Prometheus/Grafana, 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.

• 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.

careers@tekfortune.com