Senior AI QA Test Automation Engineer – CX Agent

The Role: 

We're seeking a Senior AI QA Test Automation Engineer to take a leading technical role in AI quality and test automation. You will work primarily with our AWS Bedrock AgentCore-based CX Agent Suite, a multi-agent customer-support system covering intent routing, FAQs, deposit-status queries, and human handoff. You will design and build the evaluation and QA tooling required to validate AI agents reliably, while also contributing to technical standards and best practices across QA. 

This is a Senior Individual Contributor (IC) role with a strong technical and strategic focus. You'll work closely with QA leadership, Data Science, and Engineering to architect scalable AI evaluation systems, establish technical standards, and drive the evolution of our AI evaluation framework. Our current evaluation harness is based on DeepEval and is evolving towards a broader agentic evaluation architecture. You will have the opportunity to evaluate and prototype emerging agent-orchestration approaches, including Strands Agents, and contribute to architectural decisions around their adoption. 

The main responsibilities of the position include:

Act as the primary technical enabler for QA, building scalable AI/ML frameworks, libraries, and tooling for the CX Agent Suite and beyond

Design and build AI evaluation pipelines that assess the CX Agent Suite's multi-agent responses (Intent Detector, FAQ Agent, Missing-Deposits Agent) for accuracy, relevance, tone, hallucination rate, safety/guardrail compliance, and task completion — extending or replacing the current DeepEval-based evaluation harness

Evaluate and prototype Strands Agents (or comparable agent-orchestration frameworks) for building self-evolving, autonomous QA agents, and drive the go/no-go decision on adoption

Collaborate with QA, Data Science, and Engineering to integrate AI-driven testing into the existing GitLab CI/CD pipeline (dev → test → staging → prod), alongside Terraform-provisioned, EKS/AgentCore-hosted services

Build resilient and adaptive automation that can detect and respond to changes in agent behavior, Bedrock Guardrails configuration, and routing logic

Develop data-driven quality analytics, including root-cause analysis, quality trends, and intelligent test prioritization, leveraging existing observability and evaluation data from OpenTelemetry, CloudWatch, X-Ray traces, and LangFuse offline evaluation runs

Lead research into emerging AI testing methodologies and mentor engineers through code reviews, workshops, and architectural guidance 

Main requirements:

BSc/MSc in Computer Science, AI, or a related discipline

6+ years of hands-on experience in QA/Test Automation, with strong experience designing and maintaining automation frameworks

1+ years of experience applying AI/ML in software testing or QA process improvement, with a proven track record of bringing AI/ML solutions into production workflows  

Strong Python skills, with experience working in Python/AWS-centric technology environments; Java and/or TypeScript is a plus

Hands-on experience with LLM evaluation techniques, including LLM-as-a-judge, human-in-the-loop evaluation, RAG, and multi-agent orchestration patterns  

Practical experience with DeepEval or comparable evaluation frameworks, along with familiarity with agent-orchestration SDKs such as Strands Agents

Knowledge of LLM tooling, vector databases, and MLOps pipelines

Experience integrating AI tooling into enterprise CI/CD environments, particularly GitLab, and working with containerized cloud-native environments such as Docker and Kubernetes/EKS

Strong communicator, able to influence technical decisions and drive engineering standards across teams 

The following will be considered an advantage:

Experience with autonomous QA agents or agentic orchestration frameworks for self-evolving test suites

Experience with LLM observability tools such as LangFuse, LangSmith, or Arize, particularly for measuring probabilistic and adversarial robustness  

Knowledge of AI ethics, fairness, and bias detection for guardrail and model validation

Experience with gRPC, WebSockets, and/or HTTP/2

Experience with AWS Bedrock, plus familiarity with GCP Vertex AI and/or Azure AI 

Benefit from:

Attractive remuneration package

Intellectually stimulating work environment

Continuous personal development and international training opportunities 

The Hiring Experience: What Awaits You

Let’s Connect – Intro Chat with Talent Acquisition

Deep Dive – First Interview with Your Future Team

Final Connection – Final Interview 

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