Senior AI Engineer
4 days ago
Manchester
Location: Manchester, UK Please note: This role is based in Manchester and no visa sponsorship is available. Role Overview We are looking for an experienced AI Engineer to design, build, and scale advanced AI-driven systems. You will play a key role across the full AI life cycle, working with modern LLM frameworks, retrieval-augmented generation (RAG), and agentic workflows to deliver production-ready, business-critical solutions. You'll collaborate closely with cross-functional teams, contribute to technical strategy, and support the growth of a high-performing engineering function. Key Responsibilities * Design, architect, and optimise AI-driven systems with a focus on scalability, performance, and reliability. * Implement vector and graph database solutions, including retrieval-augmented generation (RAG) architectures, for efficient information storage and retrieval. * Develop agentic reasoning workflows using frameworks such as LangChain or LlamaIndex. * Own the full AI life cycle, including data ingestion, embedding, extraction, synthesis, prompt engineering, and workflow orchestration. * Deploy, monitor, and maintain AI models in Docker-based, containerised environments. * Work closely with stakeholders and cross-functional teams to ensure AI solutions align with business objectives and deliver measurable value. * Contribute to internal knowledge sharing and mentor junior engineers within the team. Skills and Experience Required * Strong experience with Python-based frameworks, including: * FastAPI for API development * Celery for background task management * PostgreSQL for database solutions * Hands-on experience with vector and graph databases and RAG-based architectures. * Experience working with agentic and orchestration frameworks such as LangChain or LlamaIndex. * Solid understanding of large language models (LLMs), embeddings, and prompt engineering techniques. Highly Desirable * Experience designing multi-agent systems or autonomous workflows. * Practical experience deploying containerised, cloud-native tools using Docker. * Experience with advanced retrieval-augmented generation techniques, including: * TAG (Tool-Augmented Generation): Integrating external tools to enhance model capabilities. * CAG (Context-Aware Generation): Leveraging dynamic context to improve relevance and coherence. * GraphRAG (Graph-Augmented Retrieval-Augmented Generation): Using graph-based structures to enhance retrieval and reasoning. Core Competencies * Stakeholder Engagement: Works effectively with cross-functional teams to align AI capabilities with business goals and deliver meaningful outcomes. * Collaboration & Teamwork: Contributes to a growing engineering team, sharing knowledge and mentoring junior engineers. * Adaptability: Thrives in a fast-paced, evolving environment, adjusting approaches as tools, systems, and requirements change. * Continuous Improvement: Designs, optimises, monitors, and maintains AI systems to ensure long-term performance, scalability, and reliability. * Innovation: Develops and implements advanced AI architectures, including agentic workflows, vector and graph databases, and RAG techniques. * Resilience: Manages end-to-end AI delivery, from deployment through monitoring and maintenance, ensuring stability in production. * Future-Focused Mindset: Builds cloud-native, scalable AI solutions using modern frameworks to support the long-term evolution of next-generation applications.