Description

AI Architect - Generative & Agentic AI Cambridge 3 days a week We are looking for an experienced AI Architect to design enterprise AI solutions, translating complex business requirements into secure, scalable architectures You will shape how Generative AI, large language models and AI agents integrate with enterprise systems and data, working closely with business stakeholders and technical teams to guide implementation. Key Responsibilities Design end-to-end AI solution architectures covering Generative AI, LLMs and agentic workflows. Translate business processes and requirements into clearly defined AI capabilities and technical designs. Design RAG solutions across structured and unstructured enterprise data. Define integration approaches between AI services, enterprise platforms and legacy systems. Embed security, privacy, responsible AI and governance into solution designs. Produce conceptual, logical and technical architecture documentation, including sequence flows and integration patterns. Communicate architectural decisions to technical and non-technical stakeholders and support engineering teams throughout delivery. Required Experience & Skills Strong experience as an AI Architect, Solution Architect, Data/AI Architect or Enterprise Architect delivering complex enterprise solutions. Hands-on knowledge of Generative AI, LLM and agentic AI architectures. Strong understanding of RAG, embeddings, vector and hybrid search, reranking, chunking and grounding. Experience with cloud AI platforms, ideally AWS and/or Microsoft Azure. Understanding of agent orchestration and frameworks such as LangGraph, Strands or CrewAI. Strong knowledge of APIs, event-driven architecture, microservices and enterprise integration patterns. Experience designing solutions using structured and unstructured data. Understanding of AI security, responsible AI, privacy and governance. Strong architecture documentation, communication and stakeholder-management skills. Desirable Experience Amazon Bedrock, Amazon Bedrock AgentCore, Azure AI services or comparable enterprise AI platforms. Snowflake and modern AI/data architectures. Model Context Protocol (MCP) and emerging agent interoperability standards. Integration with platforms such as ServiceNow, SAP, Salesforce or Workday. AI evaluation, LLMOps/MLOps, prompt management and model/agent observability. Designing multi-agent systems and controlled autonomous workflows. To apply, please submit your CV