SDET - Test Automation AI; Agentic & LLM Systems - Perm - Circa

Overview

Help us to achieve our goal to be the global leader in total quality services. With your help we will achieve this by delivering Total Quality 360, a comprehensive suite of cutting-edge services which combine quality engineering, cyber security, conformance & interoperability and content quality to deliver end-to-end total quality solutions. Here at Resillion, our culture is based on an ‘if you see something, say something’ attitude where we take responsibility.

It’s one where we expect to adapt and embrace change as the company grows. It’s based on recognising the individual worth of every one of our employees and developing their skills to keep us all at the forefront of our industry. Above all, it’s a culture where we’re passionate about what we do, and we’re committed to the greater good of the company.

If you would like to be part of our journey, then this role may be the one for you.

SDET - Test Automation AI (Agentic & LLM Systems) - Perm - Circa £85K - 2 days a week in Glasgow.

We are seeking an SDET - Test Automation AI (Agentic & LLM Systems) to define and implement assurance approaches for AI-enabled systems. The role focuses on ensuring that AI solutions are reliable, robust, explainable, secure, and fit for purpose across their full lifecycle. The AI Assurance Engineer will assure both:

• Probabilistic components (data, models, and AI outputs)
• Deterministic components (software, integrations, and infrastructure)

and will embed assurance into automated, end-to-end delivery pipelines
. This role requires a strong understanding of how to assure AI systems holistically, rather than deep specialism in a single discipline.

AI Assurance Strategy

• Define and implement an AI assurance approach aligned to business risk and regulatory expectations.
• Provide assurance coverage across the full AI system lifecycle (design, build, deploy, operate).
• Work with engineering, data, and product teams to embed quality and risk controls early.

Probabilistic Component Assurance

• Design validation approaches for:
• Data quality and bias
• Model and prompt behaviour
• Output accuracy, relevance, and consistency
• Implement evaluation methods for:
• Drift and instability
• Hallucination and error patterns
• Support human-in-the-loop review where required.

Deterministic Component Assurance

• Assure non-AI system elements including:
• Application logic and workflows
• APIs and integrations
• Security and access controls
• Design and execute:
• Functional testing
• Non-functional testing (performance, resilience, scalability)
• Security and data protection validation

• Design & build automated assurance for AI systems.
• Integrate assurance into CI/CD and deployment pipelines.
• Implement regression and quality gates across data, models, and orchestration workflows.
• Maintain an end-to-end assurance pipeline from input data through to system outputs.

• Support monitoring and observability for AI-enabled systems in production.
• Analyse operational signals such as:
• Latency and failures
• Behaviour changes
• Performance degradation
• Contribute to incident analysis and continuous improvement of AI services.

• Define and track AI quality and risk metrics (accuracy, robustness, explainability).
• Support compliance with:
• Data protection and privacy requirements
• Responsible AI principles
• Produce clear assurance evidence for technical and non-technical stakeholders.

Core

• Strong software engineering background (Python or similar).
• Experience building automated test or validation frameworks.
• Experience working with complex distributed or cloud-based systems.

AI & Probabilistic Systems

• Understanding of:
• Data quality and bias
• Model behaviour and non-deterministic outputs
• Prompt-based or agent-based systems
• Experience validating correctness, consistency, and relevance of AI outputs.

Deterministic Systems & Non-Functional Testing

• Experience testing:
• APIs and workflows
• Cloud services
• Knowledge of:
• Performance testing
• Security testing
• Resilience and failure handling

Operational AI (MLOps / AIOps Awareness)

• Familiarity with:
• Model lifecycle management
• CI/CD for AI systems
• Monitoring and drift detection
• Understanding of production risks associated with AI systems.