Senior AI Engineer

Spencer Rose are urgently seeking an experienced Senior AI Engineer to work for our Banking client for a minimum 6 month contract (extensions highly likely), to start ASAP. Hybrid, 2 days a week in South West office.

Start: ASAP (allowing up to 2 weeks for vetting and onboarding)
Day Rate: £400-420pd Inside IR35
Hybrid: 2 days a week in the office
Duration: 6 months initially

As a Senior AI Engineer, you will design, build, and scale next-generation AI applications leveraging Large Language Models (LLMs), Agentic AI frameworks, Retrieval-Augmented Generation (RAG), and cloud-native architectures. The ideal candidate will possess strong software engineering expertise, hands-on experience with AI orchestration frameworks, and a proven ability to take AI products from prototype through production deployment.

You will work closely with product teams, architects, data engineers, and business stakeholders to deliver scalable, secure, and production-ready AI solutions that drive business value.

Key Responsibilities

AI Solution Development

• Design and develop enterprise-grade AI applications leveraging LLMs and Generative AI technologies.
• Build and deploy Agentic AI workflows using frameworks such as LangChain, LangGraph, and LlamaIndex.
• Design and implement Retrieval-Augmented Generation (RAG) architectures for knowledge-driven AI solutions.
• Develop intelligent agents capable of tool calling, memory management, reasoning, and workflow orchestration.
• Integrate AI services with enterprise systems through RESTful APIs and microservices
• Develop scalable Back End services using Python and asynchronous programming patterns.
• Design reusable AI components, SDKs, and services to accelerate solution delivery.
• Implement robust APIs with authentication, authorization, monitoring, and logging capabilities.
• Ensure software quality through unit testing, integration testing, and CI/CD pipelines.
• Deploy and manage AI applications using Docker and Kubernetes.
• Build cloud-native AI solutions on AWS, Azure, or Google Cloud Platform.
• Optimize model serving, inference performance, scalability, and cost efficiency.
• Implement observability, monitoring, and reliability practices for AI workloads.
• Design and optimize vector database solutions using Pinecone, Qdrant, Chroma, or similar technologies.
• Develop embedding pipelines, document indexing strategies, and semantic search capabilities.
• Implement data retrieval, ranking, and contextual grounding mechanisms to improve AI response quality.

The interview process will take place from next week, so APPLY NOW for immediate review.