Forward Deployed AI Engineer-Anthropic-UK

Role overview A senior forward deployed AI engineer is needed to translate enterprise business problems into secure, production-grade AI solutions built on a leading large language model platform. The role blends full-stack engineering, LLM application design, and client-facing delivery, with roughly a quarter of the time spent traveling to customer sites. It sits at the intersection of consulting, enterprise integration, and applied generative AI.

Responsibilities - Deploy, configure, and operationalize agentic AI workflows, assistants, and automations inside customer enterprise environments - Translate business requirements and operational processes into technical architectures and production-ready AI solutions - Apply prompt and context engineering, structured outputs, tool use, function calling, and human-in-the-loop review patterns - Build retrieval-augmented generation systems grounded in authorized enterprise data and knowledge sources - Improve reliability through citation patterns, validation, confidence thresholds, output schemas, and escalation workflows - Run workshops, discovery sessions, technical demonstrations, pilots, and production rollouts - Communicate AI capabilities, limitations, and tradeoffs to both technical and business stakeholders - Contribute reusable components, prompt assets, and improvements back into internal AI platforms and accelerators

Requirements - 5-8 years in software engineering, systems integration, enterprise platforms, or workflow automation - Hands-on experience with full-stack development, APIs, microservices, enterprise integrations, or cloud-native applications - Experience building LLM-powered applications, RAG systems, AI assistants, or agentic workflows - Proficiency in JavaScript or TypeScript, Python, or comparable programming languages - Familiarity with AWS, Microsoft Azure, or Google Cloud - Working knowledge of prompt engineering, embeddings, vector search, structured outputs, and model evaluation - Understanding of responsible AI concepts, including hallucination mitigation, sensitive data handling, access controls, and human oversight - Track record operating in customer-facing engineering, consulting, or technical implementation roles

Nice to have - Hands-on experience with a major enterprise workflow platform, including integration tools, virtual agents, AI agents, and platform development patterns - Familiarity with enterprise data models such as CMDB, ITSM, CSM, HRSD, knowledge management, and workflow tables - Experience with vector databases, document ingestion, chunking, and retrieval strategies - Use of orchestration frameworks such as LangChain, LangGraph, LlamaIndex, or Semantic Kernel - Exposure to AI observability, tracing, evaluation frameworks, and prompt or version management - Comfort with Docker, CI/CD, SQL, Git, infrastructure-as-code, and secure cloud deployment