Forward Deployed AI Engineer-Anthropic-US East

Role overview A senior forward-deployed AI engineering role focused on turning frontier large language model capabilities into production-ready enterprise workflow solutions. The position sits at the intersection of full-stack engineering, enterprise integration, and applied generative AI, partnering directly with customer teams to deploy, operationalize, and scale AI-powered automations. Expect a blend of hands-on technical implementation and client-facing delivery, with roughly 25% travel to customer sites.

Responsibilities - Deploy, configure, and operationalize agentic AI workflows, assistants, and automations within customer enterprise service management environments and connected technology ecosystems. - Translate business requirements and operational processes into technical architectures, implementation plans, and production-grade AI solutions. - Build retrieval-augmented generation systems grounded in authorized enterprise data, and apply prompt and context engineering, tool use, structured outputs, and human-in-the-loop review patterns. - Integrate reusable AI accelerators, agent packs, and workflow components into enterprise environments, tailoring them to client operating models, data sources, and security needs. - Lead discovery sessions, technical workshops, pilots, and production rollouts, communicating capabilities, limitations, and tradeoffs to both technical and business stakeholders. - Contribute reusable components, implementation patterns, and customer-driven feedback back into internal AI platforms and accelerators.

Requirements - 5-8 years of experience in software engineering, systems integration, enterprise platforms, or related technical delivery roles. - Hands-on proficiency in JavaScript/TypeScript, Python, or comparable languages, plus experience with cloud platforms such as AWS, Azure, or Google Cloud. - Demonstrated experience building or deploying LLM-powered applications, AI-enabled automations, RAG systems, or agentic workflows. - Familiarity with modern LLM application concepts, including prompt engineering, embeddings, vector search, tool use, structured outputs, and model evaluation. - Understanding of responsible AI practices, including hallucination mitigation, sensitive-data handling, identity controls, human oversight, and secure deployment. - Customer-facing engineering, consulting, or solutions architecture experience with the ability to navigate complex stakeholder environments.

Nice to have - Experience with enterprise service management platform development, including CMDB data models, ITSM/CSM/HRSD workflows, knowledge management, and integration patterns. - Familiarity with orchestration frameworks such as LangChain, LangGraph, LlamaIndex, or Semantic Kernel. - Experience with vector databases, document ingestion pipelines, AI observability tooling, and prompt/version management. - Comfort with Docker, CI/CD, SQL, Git, and infrastructure-as-code practices.

Benefits and work setup - Remote-first arrangement with approximately 25% travel to customer sites. - Opportunity to shape and contribute to internal AI platforms, accelerators, and reusable assets. - Inclusive, equal-opportunity workplace with accommodations available for candidates with disabilities.