AI Architect

Role: Principal AI Architect Location: London, UK Hybrid: 3 days a week from office Job Description : Principal AI Architect to lead the design and implementation of enterprise-scale AI solutions for financial services automation.

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Drive architectural decisions for LLM-based systems, agentic workflows, and intelligent document processing platforms serving private equity and fund management operations.

Required Qualifications 15 years of experience in AI/ML architecture with 8 years in enterprise AI solutions Deep expertise in LLM architectures, prompt engineering, and agentic frameworks (LangGraph, LangMem) Hands-on experience with Azure OpenAI GPT-4/5, embedding models, and Azure cloud services Strong background in Python, distributed systems, and enterprise architecture Experience with Claude Code for agentic coding and AI-powered development Proven track record in financial services or regulatory compliance environments Expert knowledge of RAG architectures, advanced RAG patterns, and vector database optimization Experience with Small Language Models (SLM), Agent-to-Agent (A2A) communication, and Model Context Protocol (MCP) Proven ability to architect and scale AI solutions for enterprise workloads (1M documents, sub-second response times) Key Responsibilities Design end-to-end AI solutions for private equity fund operations and financial automation Architect scalable agentic AI frameworks using LangGraph, LangMem, and custom agent orchestration Lead technical strategy for Azure OpenAI GPT-5 integration and advanced embedding-based retrieval systems Design and implement advanced RAG architectures including hybrid search, query routing, and contextual retrieval Establish multi-agent systems with Agent-to-Agent (A2A) communication protocols and Model Context Protocol (MCP) Architect Small Language Model (SLM) integration for specialized tasks and cost optimization Design enterprise-scale solutions supporting millions of documents with sub-second query response times Establish AI governance, model safety protocols, and regulatory compliance frameworks Lead architectural reviews for distributed AI systems, microservices, and cloud-native deployments Hands-on development using Claude Code for rapid prototyping and agentic workflows Drive architectural reviews for LlamaParse/Azure Document Intelligence integration Design fault-tolerant, high-availability AI systems with automatic failover and load balancing Establish comprehensive monitoring, observability, and performance optimization strategies Mentor technical teams and establish AI engineering best practices using modern toolchains Oversee model performance evaluation using LangGraph evals and DeepEval frameworksd02fcfcb-6df4-40ee-95cf-3eb059ce3173