Quality Engineer – AI Platform Engineering
Employment Type: Inside IR35 Contract
Duration: 6 Months
Rate: 650 – 750/ Day
Location: Central London, Hybrid (3 Days Onsite per Week)
Opportunity Overview
A global organisation is building a centralised AI enablement and platform engineering function focused on delivering scalable, secure, and governed AI capabilities across the enterprise.
This role sits within a programme delivering enterprise-grade agentic AI infrastructure, including internal AI assistants, retrieval and search services, extensibility frameworks, and governance tooling.
Programme Overview
The programme is focused on delivering a production-grade internal agentic AI platform, including:
- Development of an enterprise AI assistant capable of reasoning, planning, and tool orchestration
- Operation of enterprise retrieval, search, and grounding services for approved data sources
- Delivery of a secure internal gateway layer providing discovery, observability, policy enforcement, and lifecycle management for AI-integrated services
- Design and development of AI-integrated services and reusable capabilities that safely expose internal and third‑party systems to AI agents
- Establishment of evaluation, governance, and quality‑control frameworks to support scalable and compliant deployment of AI capabilities
The programme currently follows a centrally delivered model while evolving towards a federated contribution approach over time.
Key Responsibilities
- Define and implement evaluation frameworks covering correctness, safety, reliability, and regression impact for AI-integrated services
- Develop and maintain automated test pipelines for agentic workflows, including tool orchestration and multi‑step execution paths
- Identify, evaluate, and mitigate AI system failure modes such as hallucinations, invalid inputs, latency issues, and inappropriate tool usage
- Produce testing and governance evidence required for internal approval and operational processes
- Collaborate closely with ML Engineers and platform teams to embed testability and evaluation capabilities into AI services
- Contribute to the long‑term quality assurance and governance strategy for enterprise‑wide AI platform adoption
Essential Skills and Experience
- Strong Python development experience, particularly for automation and test frameworks
- Experience with LLM and RAG evaluation tooling, frameworks, or custom evaluation pipelines
- Expertise in automated testing across unit, integration, and regression testing environments
- Good understanding of agentic AI systems, associated risks, and operational failure modes
- Ability to assess technical solutions against governance, audit, and security requirements
- Experience working within regulated or highly governed engineering environments
What’s on Offer
- Opportunity to contribute to large‑scale enterprise AI transformation initiatives
- Exposure to cutting‑edge AI platform engineering and governance challenges
- Collaborative environment working alongside platform engineers, ML specialists, and architecture teams
- Influence over the development of long‑term AI quality and governance standards
- Opportunity to shape scalable AI engineering practices within a complex enterprise environment
Quality Engineer – AI Platform Engineering
Employment Type: Inside IR35 Contract
Duration: 6 Months
Rate: 650 – 750/ Day
Location: Central London, Hybrid (3 Days Onsite per Week)
Opportunity Overview
A global organisation is building a centralised AI enablement and platform engineering function focused on delivering scalable, secure, and governed AI capabilities across the enterprise.
This role sits within a programme delivering enterprise-grade agentic AI infrastructure, including internal AI assistants, retrieval and search services, extensibility frameworks, and governance tooling.
Programme Overview
The programme is focused on delivering a production-grade internal agentic AI platform, including:
- Development of an enterprise AI assistant capable of reasoning, planning, and tool orchestration
- Operation of enterprise retrieval, search, and grounding services for approved data sources
- Delivery of a secure internal gateway layer providing discovery, observability, policy enforcement, and lifecycle management for AI-integrated services
- Design and development of AI-integrated services and reusable capabilities that safely expose internal and third‑party systems to AI agents
- Establishment of evaluation, governance, and quality‑control frameworks to support scalable and compliant deployment of AI capabilities
The programme currently follows a centrally delivered model while evolving towards a federated contribution approach over time.
Key Responsibilities
- Define and implement evaluation frameworks covering correctness, safety, reliability, and regression impact for AI-integrated services
- Develop and maintain automated test pipelines for agentic workflows, including tool orchestration and multi‑step execution paths
- Identify, evaluate, and mitigate AI system failure modes such as hallucinations, invalid inputs, latency issues, and inappropriate tool usage
- Produce testing and governance evidence required for internal approval and operational processes
- Collaborate closely with ML Engineers and platform teams to embed testability and evaluation capabilities into AI services
- Contribute to the long‑term quality assurance and governance strategy for enterprise‑wide AI platform adoption
Essential Skills and Experience
- Strong Python development experience, particularly for automation and test frameworks
- Experience with LLM and RAG evaluation tooling, frameworks, or custom evaluation pipelines
- Expertise in automated testing across unit, integration, and regression testing environments
- Good understanding of agentic AI systems, associated risks, and operational failure modes
- Ability to assess technical solutions against governance, audit, and security requirements
- Experience working within regulated or highly governed engineering environments
What’s on Offer
- Opportunity to contribute to large‑scale enterprise AI transformation initiatives
- Exposure to cutting‑edge AI platform engineering and governance challenges
- Collaborative environment working alongside platform engineers, ML specialists, and architecture teams
- Influence over the development of long‑term AI quality and governance standards
- Opportunity to shape scalable AI engineering practices within a complex enterprise environment
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