AI Lead

We are Applus+ Laboratories, the division of the Applus+ Group that provides testing engineering, certification, and product development services. We are looking for the AI Lead to be responsible for the design, build and industrialisation of AI services and intelligent agents that help digitalise, automate and amplify the activities of the Laboratories division. The role translates business and technical needs into reliable, secure and reusable solutions that augment specialist expertise, streamline workflows and improve the quality, speed and scalability of laboratory and business operations. This is a hands-on senior engineering role, covering the full lifecycle from discovery and prototyping to production deployment, monitoring and continuous improvement. Reporting hierarchically to the Director of Technology, Labs and functionally to the Group Head of AI, the role combines delivery against Labs priorities with alignment to Group AI architecture, engineering standards and responsible AI requirements, leveraging CoreAI and shared capabilities wherever appropriate. Key Responsibilities - Translate Labs priorities into deliverable AI solutions, working with laboratory experts, operations and business teams to understand workflows, assess feasibility and define scope, acceptance criteria and measurable benefits. - Design and build AI services and intelligent agents for Labs, including knowledge retrieval, document intelligence, technical information processing, workflow automation and decision support. - Engineer agent workflows, tool integrations and orchestration, with explicit permissions, controlled execution, human approvals where required, error handling and audit trails appropriate to the business process. - Build production-grade software and integrations connecting AI capabilities to Labs applications, laboratory information management systems, document repositories and approved data sources through secure APIs and reusable components. - Select and evaluate models and AI techniques against task-specific quality, latency, cost and risk requirements. Develop retrieval-augmented generation (RAG), prompting and ML pipelines, using fine-tuning or other adaptation techniques only where justified by evidence. - Implement testing and domain validation, creating representative evaluation datasets and automated checks for accuracy, grounding, robustness and failure modes. Work with Labs experts to validate outputs and preserve the human accountability and traceability required for technical and quality-sensitive activities. - Take solutions from prototype to reliable production, applying version control, code review, automated testing, CI/CD and MLOps/LLMOps practices in collaboration with platform, DevOps and operations teams. - Leverage Group-approved platforms, patterns and components, contributing reusable services and lessons learned to the wider AI community and avoiding unnecessary duplication or isolated solutions. - Ensure that all AI services, agents and supporting components comply with Group Cybersecurity, Infrastructure and AI policies, standards and governance requirements throughout design, development, deployment and operation. - Monitor and continuously improve production quality, reliability, performance and consumption costs, investigating incidents, model or data drift and changes in user needs, and maintaining rollback and recovery mechanisms with operations teams. Experience and Qualifications - Around 8 years of relevant experience in machine learning and AI, with a strong hands-on engineering background and demonstrated ownership of solutions deployed and maintained in production. This should include recent practical experience in generative AI, LLM-based applications and intelligent agents. - Degree in Computer Science, Engineering, Mathematics or a related discipline, or equivalent practical experience. Postgraduate specialisation in AI or machine learning is an advantage. - Strong Python and software engineering skills, including API development, automated testing, version control, code review and maintainable service design. - Practical experience with ML frameworks, model evaluation, data pipelines and the development of LLM applications using RAG, embeddings, vector search, structured outputs, tool calling and agent orchestration. - Experience deploying and operating AI workloads in cloud environments, preferably Azure, using containers, CI/CD, monitoring and MLOps/LLMOps practices. - Ability to work with structured and unstructured data, SQL, enterprise APIs and access-controlled knowledge sources, with a sound understanding of data quality and security. - Professional English is required to collaborate with Group teams and international stakeholders.