AI Platform Architect – Anthropic - UK

Role overview

This is a senior engineering role focused on building and maintaining the technical foundations that power enterprise AI deployments. The position combines cloud, platform, DevOps, and applied AI engineering to move LLM-powered solutions from prototype into governed production. Work spans reference architectures, reusable platform components, and operational capabilities for AI applications built on Claude and comparable frontier models.

Responsibilities

- Design, build, deploy, and maintain scalable platform capabilities supporting enterprise AI, ML, LLM, retrieval-augmented generation (RAG), and agentic AI applications. - Develop reusable reference architectures, infrastructure patterns, deployment templates, and engineering standards for an internal AI foundry practice. - Build secure integrations connecting AI applications to enterprise data sources, APIs, workflow systems, and authorized tools. - Partner with AI architects and forward-deployed engineers to translate client needs into reliable, supportable platform designs. - Maintain secure integrations with Claude-family models and related AI services, including authentication, prompt and context management, structured outputs, tool use, logging, error handling, and rate-limit management. - Implement CI/CD, infrastructure-as-code, monitoring, and LLMOps practices that improve deployment speed, quality, and operational maturity. - Contribute reusable components, reference architectures, and playbooks to internal intelligence, data, and agent-pack platforms.

Requirements

- 5+ years of experience in platform engineering, cloud engineering, DevOps, software engineering, data engineering, or systems integration. - Hands-on experience designing and deploying cloud-native applications on AWS, Microsoft Azure, and/or Google Cloud Platform. - Strong experience with CI/CD, Git-based workflows, automated testing, infrastructure as code, and production release processes. - Containerization and orchestration experience with Docker, Kubernetes, or comparable serverless or cloud-native platforms. - Proficiency in Python, JavaScript/TypeScript, Java, Go, or Bash, plus experience designing and consuming REST APIs with authentication and authorization patterns. - Hands-on experience with LLM-powered applications, RAG systems, prompt and context engineering, embeddings, vector search, structured outputs, tool or function calling, evaluations, and model monitoring.

Nice to have

- Familiarity with vector databases and enterprise search platforms such as Pinecone, Weaviate, pgvector, OpenSearch, Elasticsearch, or Azure AI Search. - Infrastructure tooling experience with Terraform, Pulumi, CloudFormation, Bicep, Helm, Argo CD, GitHub Actions, GitLab CI, Azure DevOps, or Jenkins. - ServiceNow architecture, APIs, IntegrationHub, Flow Designer, Virtual Agent, Now Assist, AI Agents, CMDB, or enterprise data-integration experience. - FinOps practices for optimizing cloud, model, inference, storage, and data-processing costs. - Background in consulting, professional services, enterprise architecture, or client-facing technical delivery. - Relevant certifications in cloud, Kubernetes, security, data engineering, ServiceNow, or AI/ML. - Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related technical discipline.