Forward Deployed AI Engineer-Anthropic-Netherlands/Western Europe

Role

NewRocket is hiring a Senior Forward Deployed AI Engineer on the AI Foundry team to work directly with customers to deploy, operationalize, and scale AI-powered workflow solutions using Anthropic/Claude and other LLM platforms. This role combines full‑stack engineering, enterprise integration, generative AI implementation, prototyping, and client-facing solution delivery.

Location

Remote with travel (~25%).

Key Responsibilities

• Deploy, configure, and operationalize agentic AI workflows, assistants, automations, and AI-powered integrations within customer ServiceNow environments and broader enterprise ecosystems.
• Translate business requirements and processes into technical architectures, implementation plans, and production-ready AI solutions.
• Build enterprise AI applications and workflows using Claude, the Anthropic API, and other LLM platforms; apply prompt/context engineering, tool/function calling, structured outputs, and long-context management.
• Design and implement RAG (retrieval-augmented generation) solutions that ground responses in enterprise data and knowledge sources.
• Create demos, prototypes, and proofs-of-concept to validate AI-driven workflows and iterate rapidly with customers.
• Develop secure integrations, microservices, scripts, automation logic, and lightweight UIs connecting ServiceNow, enterprise systems, APIs, and data platforms.
• Implement engineering best practices for authentication, authorization, secrets management, logging, error handling, version control, observability, and production readiness.
• Establish evaluation and testing approaches (test cases, datasets, success metrics, regression testing) and implement safeguards for responsible AI (data protections, human-in-the-loop patterns, prompt injection mitigation).
• Contribute reusable assets, Agent Packs, accelerators, prompt libraries, and platform feedback to NewRocket’s AI products and delivery playbooks.

Requirements

• 5–8 years of experience in software engineering, systems integration, enterprise platforms, workflow automation, or related technical delivery roles.
• Hands-on experience in full‑stack development, scripting, APIs, microservices, enterprise integrations, or cloud-native applications.
• Experience building, deploying, or supporting AI/LLM-powered applications, conversational experiences, RAG systems, or agentic workflows.
• Experience integrating APIs, enterprise applications, data platforms, or workflow systems in production environments.
• Proficiency in JavaScript/TypeScript and/or Python.
• Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
• Familiarity with LLM application concepts: prompt engineering, context windows, token usage, embeddings, vector search, RAG, tool use/function calling, structured outputs, and model evaluation.
• Understanding of responsible AI topics: hallucination mitigation, sensitive-data handling, identity and access controls, human oversight, AI safety, and secure AI deployment.
• Customer-facing engineering, consulting, technical implementation, or solutions architecture experience; strong communication and problem-solving skills.
• Ability to manage ambiguity, prioritize effectively, and travel approximately 25%.

Preferred Qualifications

• Hands-on experience with Anthropic technologies (Anthropic API, Claude models, Anthropic Console, Claude Code) and completion of Anthropic partner/technical training where available.
• Familiarity with Model Context Protocol (MCP) concepts and secure tool/data integration patterns for AI applications.
• ServiceNow development/configuration experience (scripting, IntegrationHub, Flow Designer, Virtual Agent, Now Assist, AI Agents) and knowledge of CMDB, ITSM/CSM/HRSD workflows.
• Experience with vector databases, embeddings, document ingestion and chunking, retrieval strategies, and knowledge-grounding patterns.
• Familiarity with LLM orchestration and application frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel) and AI observability/evaluation frameworks.
• Experience with Docker, CI/CD, SQL, Git, and infrastructure-as-code practices.

What Success Looks Like in the First 6 Months

• Multiple deployed AI-powered workflows, assistants, automations, or integrations in customer environments.
• Trusted relationships with customer technical teams and business stakeholders; demonstrable application of Claude and modern LLM engineering practices.
• Reusable components, prompt assets, and improvements contributed to NewRocket’s AI platforms and accelerators.
• Methods established for moving AI use cases from prototype to governed production deployment.

Removed/Not Included

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