Forward Deployed AI Engineer-Anthropic-US West

Role overview

Build and deploy AI-powered workflows that address practical business needs in customer environments. The work combines software engineering, enterprise integration, generative AI implementation, and direct collaboration with technical and business stakeholders. You will also help improve reusable AI components and practices across engagements. The role is remote and involves approximately 70% travel.

Responsibilities - Turn customer processes and goals into technical designs, delivery plans, and production-ready AI solutions. - Configure AI assistants, agentic workflows, and automations in enterprise platforms and connected systems. - Build LLM applications using techniques such as retrieval-augmented generation, tool use, structured outputs, and human review. - Integrate AI workflows with enterprise APIs, data sources, and business applications, taking security and access requirements into account. - Support discovery workshops, demonstrations, pilots, and production launches; explain solution behavior, limitations, and tradeoffs. - Share reusable components and customer feedback to improve internal platforms, accelerators, and delivery practices.

Requirements - 5–8 years of experience in software engineering, systems integration, workflow platforms, or related technical delivery. - Strong hands-on skills in full-stack development, scripting, APIs, microservices, integrations, or cloud applications. - Experience building or supporting AI-enabled applications, automations, conversational tools, retrieval systems, or agent workflows. - Proficiency in JavaScript, TypeScript, Python, or a similar programming language; experience with a major cloud platform. - Understanding of LLM application concepts such as prompt and context design, embeddings, vector search, tool calling, structured outputs, and evaluation. - Awareness of responsible AI practices, including sensitive-data handling, access controls, human oversight, and methods to reduce unreliable outputs.

Nice to have - Experience with enterprise workflow platforms, knowledge management, or service management data and processes. - Familiarity with LLM orchestration frameworks, vector databases, AI observability and evaluation, Docker, CI/CD, SQL, Git, or infrastructure as code. - Experience working directly with customers in engineering, consulting, implementation, or solution architecture.