Quality Assurance Engineer

Xpansiv is the leading infrastructure provider for the energy transition markets.   Our comprehensive platform includes registries, online marketplaces, market execution services, wholesale power solutions, and market data for energy and environmental commodity markets. Trusted worldwide, Xpansiv enables stakeholders to deliver transparent, credible, and auditable environmental claims to address the growing global demand for assurance and accountability on climate action and sustainability performance.   From our founding in 2009 through more than 10 acquisitions, Xpansiv has become a global leader in environmental commodity markets.  We are backed by Blackstone and other leading investors.
 

Position Summary:  

Seeking a QA AI Engineer to perform testing and quality assurance for systems, applications, and AI-enabled products developed by Xpansiv. This role is responsible for ensuring the quality, reliability, accuracy, and safety of Xpansiv’s AI-driven products and proprietary LLM infrastructure. The QA AI Engineer will create test plans, document and execute test cases, build automated and semi-automated evaluation frameworks, and validate both deterministic software behavior and non-deterministic AI outputs. This role works closely with AI Engineering, Product, Operations, Engineering, business analysts, business owners, and subject matter experts to build quality into AI products from the start and provide the human-in-the-loop assurance required by Xpansiv’s AI governance standards.  

Key Responsibilities:  

• Create test plans for AI-powered and traditional software applications, including manual testing, automated testing, performance testing, regression testing, security testing, and end-to-end testing 

• Formulate and document test cases based on product requirements, user stories, acceptance criteria, AI governance standards, and business workflow expectations 

• Execute test cases through targeted manual testing, automated testing, exploratory testing, and AI-specific evaluation methods 

• Design, build, and maintain evaluation pipelines for AI-powered applications across Xpansiv business lines 

• Develop evaluation datasets, golden sets, and scenario suites that measure accuracy, consistency, structured-output quality, policy adherence, and business-rule compliance of LLM outputs 

• Detect, document, and reproduce AI-specific failure modes, including hallucinations, prompt injection, inconsistent outputs, formatting errors, data leakage, bias, unsafe responses, and model or prompt-update regressions 

• Build automated and semi-automated AI evaluation frameworks, including model-graded assertions, regression harnesses, prompt test suites, and quality scorecards, alongside traditional QA automation