AI Engineer (Expert)- AI, LLMs, RAG and Agentic AI

An exciting opportunity is available for an Expert AI Engineer to join a global technology environment focused on the industrialisation of emerging AI technologies and enterprise-scale AI solutions. This role is suited to a highly experienced professional with expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, Machine Learning, and MLOps, who can design and deliver production-grade AI solutions in a fast-paced, innovative environment.

Essential Skills

• Architect and deliver production-grade AI systems spanning classical ML, LLM pipelines, RAG, agentic orchestration, and context engineering.
• Design and implement retrieval-augmented generation (RAG) architectures, including semantic caching, contextual compression, hybrid retrieval and embedding pipelines.
• Provide consultation on the architecture of agentic systems, including tool-calling architectures, multi-agent workflows, agent memory design, and human-in-the-loop integration.
• Develop and optimize large language model (LLM) capabilities: prompt and context engineering, fine-tuning, LLM orchestration, and document intelligence workflows.
• MLOps and delivery: model deployment, inference gateways, intelligent routing, monitoring, governance, and performance optimisation for production systems.
• Data engineering and production-scale processing: PySpark, ETL/ELT pipelines, API ingestion, unstructured data processing, vector stores, and scalable embedding generation pipelines.

Advantageous Skills

• Experience implementing governance, security and compliance measures for AI in regulated, high-impact domains.
• Knowledge of BI and visualization tools (e.g., Power BI, Grafana, AWS QuickSight) to surface model outputs and monitoring metrics.
• Familiarity with LLM agents, RAG pipelines and retrieval tooling for document intelligence solutions.
• Experience with CI/CD and automation tools such as GitHub Actions and orchestration platforms for model lifecycle management.
• Background in prompt engineering and contextual window design for cost-effective inference and improved relevance.
• Experience coaching teams and delivering training to promote adoption of AI solutions across business units.
• Prior experience in AI ethics, bias mitigation, and explainability for enterprise-grade models.

• Lead design and delivery of end-to-end AI solutions including classical ML models, LLM pipelines, RAG systems, and multi-agent orchestration.
• Provide architectural consultation for agentic systems, advising on tool-calling patterns, agent coordination (A2A), memory design, and human-in-the-loop workflows.
• Design context windows, prompt strategies, and contextual compression techniques to optimise LLM relevance and cost.
• Implement document intelligence solutions leveraging embeddings, vector stores, and hybrid retrieval strategies.
• Architect and build agentic systems: tool-calling architectures, multi-agent workflows, agent memory, and human-in-the-loop pathways.

• Bachelor's degree in Computer Science, Data Science, Mathematics or equivalent experience, with strong background in mathematics and analytical problem solving.
• Proven track record of leading teams, platforms and enterprise deployments handling millions of records in regulated, high-impact environments.
• Deep hands-on experience across the full lifecycle of modern LLM applications: design, implementation, orchestration, deployment and operational monitoring.

Interested candidates who meet the above requirements are invited to send their updated CVs to ***email_hidden***