Senior Product Software Engineer - AI Quality Engineering

Wolters Kluwer•Cape Girardeau, MO

1d•$83,400 - $145,650•Onsite

About The Position

We are seeking an experienced Senior AI Quality Engineer to own and implement quality strategy, evaluation systems, and reliability practices for AI-powered product experiences in Wolters Kluwer Tax and Accounting. In this role, you will architect, build, and maintain evaluation systems that measure retrieval accuracy, citation correctness, response quality, safety, performance, and overall alignment with customer intent. You will work hands-on across test automation, AI evaluation, APIs, data pipelines, observability, and release readiness while partnering with software engineers, product managers, UX, and stakeholders to ensure our generative AI systems are trustworthy, observable, secure, and ready for production use in professional tax and accounting workflows. This role goes beyond validating that features function as designed. You will own the practical quality engineering implementation for non-deterministic AI systems: defining how quality is measured, building automated evaluation and regression capabilities, implementing quality gates in delivery pipelines, driving release-readiness decisions, mentoring team members in AI quality practices, and establishing the standards customers can trust.

Requirements

5+ years of experience in quality engineering, SDET, software engineering, test automation, reliability engineering, or equivalent engineering roles.
2+ years of hands-on experience testing, evaluating, or validating AI/ML, LLM, RAG, or other non-deterministic systems in production or production-like environments.
Strong proficiency with Python test automation frameworks such as Pytest, unittest, or equivalent, including experience building reusable test harnesses and regression suites.
Experience validating REST APIs, microservices, service integrations, data ingestion pipelines, and end-to-end web or chat-based workflows.
Practical understanding of RAG systems, vector search, embeddings, prompt behavior, model evaluation, groundedness, citation quality, and AI failure modes.
Experience defining quality metrics and success criteria where outputs are probabilistic rather than strictly deterministic.
Knowledge of CI/CD pipelines, Git-based workflows, automated regression testing, release gates, and modern engineering practices.
Experience with observability, logging, tracing, dashboards, and metrics used to monitor production behavior and diagnose system failures.
Ability to navigate ambiguity, decompose complex AI behaviors into testable risks, and communicate findings clearly to technical and non-technical stakeholders.
Strong collaboration skills in Agile environments, with demonstrated ability to influence engineering quality practices across product, design, development, and domain expert teams.
Commitment to responsible AI quality practices, including trust, transparency, security, privacy, fairness, and customer impact.

Nice To Haves

Experience with LLM observability, tracing, and evaluation platforms such as LangSmith, Langfuse, TruLens, Arize/Phoenix, or custom evaluation and monitoring tooling.
Experience testing RAG pipelines using vector databases or search platforms such as Azure AI Search, Pinecone, Weaviate, Elasticsearch, or similar technologies.
Experience with LLM application frameworks such as LangChain, LlamaIndex, Semantic Kernel, or custom orchestration approaches.
Experience with Azure OpenAI Service, Azure AI Studio, Azure cloud services, or enterprise AI deployments.
Experience with containerized environments and cloud-native delivery practices using Docker, Kubernetes, and related tooling.
Experience with performance, load, or resiliency testing tools such as Locust, k6, JMeter, or equivalent.
Experience with production system observability tooling such as Azure Application Insights, Grafana, Prometheus, Datadog, OpenTelemetry, or equivalent.
Knowledge of LLMOps, model monitoring, A/B testing, human-in-the-loop evaluation, or production feedback-loop design.
Experience validating AI applications that handle sensitive, regulated, or professional-domain data.
Background in tax, accounting, legal, financial, or other domains where answer accuracy and explainability are critical.

Responsibilities

Own and implement the quality engineering approach for enterprise-scale generative AI applications, including RAG pipelines, LLM orchestration, agentic workflows, APIs, ingestion pipelines, and AI-driven user experiences.
Architect and implement evaluation harnesses that measure retrieval accuracy, citation correctness, answer relevance, groundedness, hallucination rate, safety, latency, cost, and end-to-end system behavior.
Design automated regression suites that detect quality drift across prompts, embeddings, chunking strategies, model versions, retrieval configuration, orchestration logic, and application workflows.
Partner with developers to validate AI system architecture, service integrations, API behavior, data ingestion, observability, resilience, security controls, and production readiness.
Lead root-cause analysis for AI quality failures using logs, traces, evaluation outputs, feedback signals, customer scenarios, and domain-specific acceptance criteria.
Define, implement, and maintain measurable quality gates for AI releases, and advise product and engineering leaders on risk, readiness, and tradeoffs across accuracy, reliability, latency, cost, and user trust.
Develop test automation in Python and related frameworks for APIs, services, chat workflows, data pipelines, and AI evaluation workflows within CI/CD pipelines.
Build and maintain observability practices and dashboards that track model behavior, retrieval performance, operational health, quality trends, and customer-impacting failure modes.
Contribute to responsible AI practices including bias and fairness checks, content safety validation, prompt injection testing, data privacy validation, and compliance-oriented evidence collection.
Collaborate with product managers, UX, subject matter experts, and stakeholders to translate customer workflows and business requirements into testable AI quality criteria.
Mentor engineers and quality team members in AI evaluation methods, non-deterministic testing strategies, automation design, and production-quality engineering practices.
Stay current with emerging AI quality, LLM evaluation, RAG assessment, and LLMOps practices through research, experimentation, and community engagement.