Senior QA Engineer

Salary: £59,000 - 99,000 per year

Requirements:

• We require 3+ years of experience in QA, data analysis, or a similar role where attention to detail and data accuracy mattered; internships and placement years count.
• We require 1+ years of experience using agentic tools for QA.
• We require basic knowledge of agentic technologies.
• We need a sharp eye for detail and the ability to spot inconsistencies.
• We look for an analytical mindset and the ability to understand why issues happen.
• We need strong organisational skills to manage work across multiple projects.
• We look for clear communication skills, both written and verbal, with technical and non-technical audiences.
• We need comfort with ambiguity and a willingness to help shape evolving processes.
• Nice to have: exposure to AI-generated content, NLP outputs, or taxonomy/classification systems.
• Nice to have: interest in HR technology, talent management, or how organisations think about skills and roles.
• Nice to have: experience defining what good looks like for data, such as writing acceptance criteria or quality metrics.
• Nice to have: experience setting up a process or workflow from scratch, even informally.

Responsibilities:

• We review and improve AI-generated role profiles against our quality standards.
• We spot errors, gaps, and inconsistencies in the data and fix them while keeping the overall structure intact.
• We work with the Professional Services team to manage the QA stage of each project and keep delivery on track and within agreed timelines.
• We review our taxonomies and ensure the accuracy of skills, tasks, and role titles.
• We use AI agentic tools like Claude to review output and implement deterministic agentic flows to accelerate and scale our QA activities.
• We flag anything that needs a decision before sign-off and communicate clearly about the changes we make and why.
• We collaborate with the Data Science team on defining, implementing, and tracking QA metrics to improve AI output quality.
• We keep a clear record of issues found during QA, what went wrong, how often it happens, and likely causes so the Data Science team can improve the AI pipeline.
• We help test pipeline updates between projects to confirm fixes work as expected and do not introduce new problems.
• We turn QA findings into straightforward recommendations the team can act on.
• We write and maintain documentation for our QA workflows so new team members can get up to speed quickly.
• We spot gaps in our current quality checks and suggest improvements to review steps, quality measures, and standards.

Technologies:

• Agentic AI
• AI
• Support
• SAP
• Flow

More:

We are Beamery, and our jobs, skills and tasks data platform helps organisations navigate AI-driven change and make more informed talent decisions across the talent lifecycle. Our solutions support recruitment, mobility, upskilling, diversity, work architecture, and workforce planning for forward-thinking companies. This is an early hire in our growing Engineering team, based in London, UK in a hybrid, full-time role, and the position sits at the intersection of Data Science and customer delivery. We value a culture built on trust, empathy, and honesty, and we are an equal opportunity employer committed to building a representative, inclusive, and engaging environment. We also have deepening integrations with SAP, Workday, Microsoft, and LinkedIn, and are advancing our use of agentic AI, proprietary LLMs, and knowledge graph technology to help organisations unlock broader talent pools and expand access to opportunity at scale.

last updated 35 week of 2026