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.