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## Job details - Location: Caversham, Reading - Work mode: Onsite - Employment type: Full-time (Not an internship) - Salary: GBP 39,000 - 45,000 per year ## Role overview Tec Partners is hiring a Quality Engineer - AI in Caversham, Reading. This full-time, onsite role focuses on quality assurance for AI systems, particularly LLM and agentic AI applications. The position offers a competitive salary of £39,000 - £45,000 per year and requires strong Python automation skills combined with expertise in AI evaluation frameworks and testing methodologies. ## Job details This Quality Engineer position is based in Caversham, Reading and is an onsite role. The successful candidate will work on automated testing pipelines for AI systems, ensuring robust evaluation of large language models and retrieval-augmented generation systems. The role involves assessing technical solutions against governance and security requirements while building comprehensive test frameworks. Salary range is £39,000 - £45,000 GBP per year.

## Job details - Location: Caversham, Reading - Work mode: Onsite - Employment type: Full-time (Not an internship) - Salary: GBP 39,000 - 45,000 per year ## Role overview Tec Partners seeks a Quality Engineer specializing in AI systems. The role focuses on designing and implementing automated testing frameworks for large language models (LLMs), RAG systems, and agentic AI architectures. Candidates will evaluate technical solutions against governance, security, and operational risk standards while developing evaluation pipelines for AI performance. ## Job details Based in Caversham, Reading, this full-time role offers a salary of £39,000 to £45,000 annually. The position is onsite and does not include internship components.

## Role overview Tec Partners seeks a Quality Engineer with AI expertise to design and implement robust testing frameworks for LLM and agentic AI systems. This role requires advanced automation skills and a deep understanding of AI evaluation pipelines. ## Key responsibilities - Develop Python-based test frameworks for LLM/RAG evaluation - Design automated testing strategies across unit, integration, and regression environments - Assess AI system risks and operational failure modes - Validate technical solutions against governance, audit, and security standards - Collaborate with engineering teams to optimize evaluation pipelines