Applied AI Engineer

The Opportunity We are working with a highly successful, globally established organisation that is making significant investments in Artificial Intelligence and next-generation technology. The company is building a sophisticated, enterprise-wide AI platform designed to transform how its engineering and technology teams develop, deploy, and leverage AI-powered solutions. We are seeking an Applied AI Engineer to join a specialist AI Architecture team responsible for shaping the technical direction of this platform. This is a highly technical, hands-on position combining AI engineering, experimentation, software development, and systems architecture. Rather than simply implementing existing AI solutions, you will help determine which approaches work best, designing and executing experiments that influence critical architectural decisions. You will work closely with senior technical stakeholders, exploring emerging technologies and translating experimental findings into scalable, production-ready systems. Key Responsibilities - AI/ML Experimentation: Design, develop, and execute large-scale experiments to evaluate AI technologies, architectures, and engineering approaches. Establish clear hypotheses, benchmarks, success criteria, and reproducible results. - LLM & Agentic AI Systems: Investigate and evaluate advanced AI capabilities, including Large Language Models (LLMs), agentic workflows, tool orchestration, and AI execution frameworks. - Retrieval & Context Engineering: Explore and optimise retrieval systems, embeddings, context management strategies, and techniques for improving AI application performance. - AI Platform Development: Design and contribute to production-grade AI infrastructure, including evaluation frameworks, retrieval services, model routing, and monitoring capabilities. - Performance Optimisation: Assess AI system performance, scalability, reliability, and efficiency, identifying opportunities to improve architecture and execution. - Technical Research & Evaluation: Investigate emerging AI tools, frameworks, and methodologies, providing evidence-based recommendations on technology selection and architectural direction. - Architecture & Engineering Collaboration: Work closely with platform architects and engineering teams to translate research findings into practical, scalable solutions. - Technical Documentation: Produce clear technical reports, experimental findings, and recommendations that inform platform strategy and engineering decisions. Required Skills & Experience - Strong software engineering background, with advanced proficiency in Python. - Hands-on experience developing AI/ML applications, infrastructure, or supporting systems. - Solid understanding of machine learning fundamentals, including statistical learning, classical ML techniques, transformers, and modern LLM architectures. - Experience with AI-related infrastructure such as retrieval systems, evaluation frameworks, model monitoring, or sandbox execution environments. - Strong understanding of systems programming, performance optimisation, and scalable software architecture. - Familiarity with modern AI development approaches, including agentic systems, tool orchestration, embeddings, and context engineering. - Ability to design rigorous technical experiments, establish benchmarks, interpret results, and communicate findings effectively. - Understanding of AI platform architecture and the ability to translate research and experimentation into practical engineering decisions. - Strong analytical and problem-solving capabilities, with a willingness to explore technically complex problems where established solutions may not exist. The Ideal Candidate We are particularly interested in engineers who combine strong software engineering capabilities with a research-oriented mindset. You may currently be working as an Applied AI Engineer, Machine Learning Engineer, AI Platform Engineer, Research Engineer, or Software Engineer specialising in AI infrastructure. You enjoy understanding how technologies work beneath the surface, challenging assumptions, benchmarking alternative approaches, and building solutions grounded in measurable results. You are equally comfortable conducting technical experiments and developing robust, production-quality software.