Artificial Intelligence Engineer

Role - AI Engineer Location - London, UK (Hybrid) Type - Contract Job Description: Role summary We are looking for an AI Engineer to design, build, integrate, and optimize AI-powered services and intelligent systems that enhance employee experience and support real-world HR technology use cases. The role is hands-on and engineering-focused, with emphasis on secure, scalable, measurable, and maintainable AI application delivery. Role type - Hands-on AI engineering resource Primary focus - AI services, LLM integration, RAG patterns, prompt/context pipelines, evaluation, and Azure AI services Key responsibilities • Design and develop AI-powered services that enhance employee experience and support HR technology use cases. • Integrate and optimize large language models and intelligent systems using Azure OpenAI and other cloud-native AI tools. • Apply advanced AI architecture patterns such as Retrieval-Augmented Generation (RAG), Agentic RAG, MCP, Function Calling, and A2A to practical enterprise use cases. • Engineer robust pipelines for prompt design, context handling, embeddings, chunking strategies, and real-time data integration. • Evaluate, test, and optimize model output and application performance to improve relevance, robustness, fairness, and explainability. • Implement guardrails, prompt testing, adversarial and bias testing, and other controls needed for responsible AI application delivery. • Develop and deploy cloud-based AI applications at scale using Azure Cloud Services for AI, including Azure OpenAI and Azure AI Search. • Ensure solutions are secure, reliable, observable, maintainable, and well documented. Required skills and experience • Excellent Python skills and hands-on experience • Experience in AI application development, with focus on cloud-based AI model integration, deployment, and optimization. • Experience with AI/ML and agentic application frameworks such as LangChain, LangGraph, Pydantic • Proficiency in advanced AI architecture patterns, including RAG, Agentic RAG, MCP, Function Calling, and A2A, especially in an Azure environment • Good understanding of GPT token usage, latency analytics, and budget guardrails. • Sound understanding of AI guardrails, prompt fuzzing, adversarial testing, and bias testing. • Experience in prompt engineering, context engineering, vector databases, embedding and chunking strategies, and real-time data integration. • Experience evaluating model output and optimizing AI application performance. • Hands-on experience with Azure Cloud Services for AI, including Azure OpenAI and Azure AI Search. • Strong commitment to quality, maintainability, documentation, and continuous learning. Nice to have • Understanding of alignment and feedback techniques, synthetic data generation, and continuous human-in-the-loop review loops. • Experience designing evaluation approaches for relevance, groundedness, explainability, safety, robustness, and operational quality. • Experience packaging AI features for production use with logging, monitoring, observability, and controlled rollout patterns. Profile we are looking for • A pragmatic, hands-on AI engineer who can build production-grade AI services, integrate LLM capabilities into enterprise applications, and engineer reliable prompt, retrieval, context, evaluation, and deployment pipelines. The ideal candidate is technically strong, delivery-oriented, quality-minded, and comfortable working on secure and scalable AI applications in a cloud environment.