QA Engineer

QA Engineer

Contract type: Permanent
Location: London
Working style: Hybrid 50% home/office based
Closing date: Tuesday 17thMarch

We have a new opportunity for a QA Engineer to join us at RLAM as part of our AI acceleration. Were looking for a technically minded individual who is early on in their career of software testing with a strong foundation in Artificial Intelligence & Machine Learning. The successful candidate will be someone who can elevate quality across RLAMs datadriven and AIenabled solutions combined with an analytical mindset who is curious and has a willingness to experiment and learn in a fast-paced environment. Youll build robust testing frameworks for complex data pipelines and work closely with technical and business teams to ensure every AIpowered deliverable is reliable wellvalidated and ready for production.

About the role

• Test RLAM software products to confirm they meet business requirements especially those involving data engineering.
• Support the creation and maintenance of test scripts for test automation frameworks that support our data BI and analytics tools.
• Work with other tech teams to check that data pipelines run smoothly across different data sources.
• Collaborate with data engineers data modellers and analysts to put reliable testing processes in place covering data quality validation and reporting.
• Create and improve reusable test scripts that fully cover business needs and help us meet quality risk and audit standards.
• Review business requirements and user stories to ensure they are clear and testable.
• Support the Data QA Lead by contributing ideas and feedback to help continuously improve our QA approach and frameworks including UAT support.

About you

Qualification

• A degree in Computer Science Artificial Intelligence Engineering Mathematics or a related field

• We encourage all students to apply from recent graduates through to MSc/PhD

• Please note we cannot sponsor visas

Skills

• Strong analytical mindset and ability to interpret complex requirements.
• Good understanding of modern AI approaches including LLMs and RetrievalAugmented Generation (RAG) and how these models are designed evaluated and integrated into applications.
• Ability to turn business requirements into clear structured test cases.
• Capable of spotting issues and identifying solutions to help get them resolved.
• Good at providing clear timely updates on progress and raising issues when needed.
• An understanding of how to plan and estimate testing work.
• Confident working with SQL to query and analyse structured data plus familiarity with REST APIs andOpenAPIstandards for building and consuming modern services.
• Confident using Windows-based tools and systems.

Desirable

• Software Testing or Development experience.
• Knowledge of Agentic AI
• Knowledge of Devops and CI/CD
• Prior internship or work experience in a Data ScienceAIor Machine Learningenvironment.

About Royal London Asset Management

Royal London Asset Management (RLAM) part of the Royal London Group is one of the UKs leading fund management companies working with a wide range of clients across the globe to achieve their investment goals. Our long-term client-driven focus means that we have a long-standing commitment to responsible investment. We act as responsible stewards of our clients capital exercising their rights and influencing positive change.

Our People Promise to our colleagues is that we will all work somewhere inclusive responsible enjoyable and fulfilling. This is underpinned by our Spirit of Royal London values; Empowered Trustworthy Collaborate Achieve.

Weve always been proud to reward employees by offering great workplace benefits such as 28 days annual leave in addition to bank holidays an up to 14% employer matching pension scheme and private medical insurance.

Inclusion diversity and belonging

Were an Inclusive employer. We celebrate and value different backgrounds and cultures across Royal London. Our diverse people and perspectives give us a range of skills which are recognised and respected whatever their background.

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