Senior AI Engineer

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.

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.