AI Engineer - Hybrid

• Design, develop, and maintain end-to-end AI solutions spanning front-end interfaces, back-end services, and data pipelines
• Build, optimise, and deploy AI and machine learning models to ensure solutions are scalable, maintainable, and production-ready
• Deliver Generative AI, Agentic AI, and classical machine learning solutions aligned with enterprise requirements
• Integrate AI systems with existing enterprise platforms to ensure stability and seamless operation
• Collaborate with data scientists, engineers, and business stakeholders to identify opportunities and deliver robust solutions
• Provide technical guidance and mentorship to junior engineers, promoting best practices in AI development
• Lead the implementation of engineering standards across AI and ML delivery
• Stay current with emerging AI technologies and contribute to continuous innovation

Technologies:

• Agentic AI
• AI
• Cloud
• Fine-tuning
• Machine Learning
• MLOps
• Python
• RAG, We are a globally renowned organisation seeking an AI Engineer to join our high-performing technology function, delivering intelligent, production-grade AI solutions across enterprise environments. This hands-on role combines full-stack engineering with advanced AI system development, focused on scalable, secure solutions that deliver measurable business value. The role is suited to an experienced engineer with strong software foundations and practical expertise across Generative AI, Agentic AI, and machine learning, who enjoys working with stakeholders and contributing to complex delivery initiatives. We offer a salary of up to 80,000 plus benefits. This is a full-time, Monday-to-Friday role, hybrid, with three days per week onsite in London.

• Strong proficiency in Python and extensive experience with AI, machine learning, and NLP libraries
• Hands-on experience with modern large language models, including prompt engineering, fine-tuning, and evaluation
• Practical experience with Generative AI and agent-based AI frameworks
• Strong experience with MLOps and LLMOps tooling, including model lifecycle management
• Proven experience deploying solutions on major cloud platforms, including their AI and ML services
• Strong foundation in software engineering principles for scalable, production-grade systems
• Experience designing and delivering enterprise AI solutions, including RAG architectures using vector databases
• Proven experience delivering full-stack AI or ML systems in enterprise environments
• Strong understanding of advanced agent architectures, reasoning systems, and autonomous workflows
• Excellent communication and stakeholder management skills
• Experience supporting proposals, client-facing discussions, or technical presentations