Forward Deployed AI Engineer-Anthropic-US West

Forward Deployed AI Engineer

AI Foundry | NewRocket

Location: Remote with travel (~70%)
Reports to: Global AI Center of Excellence Lead

Role Overview

NewRocket is seeking a highly skilled SeniorForward Deployed AI Engineer to join the AI Foundry team and work directly with customers to deploy, operationalize, and scale AI-powered workflow solutions.

This role blends full-stack engineering, enterprise integration, generative AI implementation, and client-facing solution delivery. Forward Deployed AI Engineers partner closely with business consultants, product teams, AI/ML engineers, and customer stakeholders to translate real-world business problems into secure, reliable, deployable AI-driven solutions.

As an Anthropic partner/vendor, NewRocket is expanding its capability to design and deliver enterprise solutions using Claude and other leading AI technologies. In this role, you will apply modern LLM engineering practices-including prompt and context engineering, retrieval-augmented generation (RAG), tool use, structured outputs, agentic workflows, model evaluation, and responsible AI controls-to deliver measurable customer value.

You will help customers implement agentic AI workflows, intelligent automations, and AI-powered integrations within ServiceNow and broader enterprise ecosystems. You will also contribute directly to the evolution of NewRocket's AI platforms, accelerators, and intellectual property, including the NewRocket Intelligence Platform, Value Realization Dashboard, Data Intelligence Platform, and reusable Agent Packs.

This role requires strong engineering skills, curiosity about emerging AI technologies, sound judgment regarding responsible AI deployment, and the ability to operate effectively in fast-moving customer environments.

Key Responsibilities

Client Delivery & AI Solution Implementation

• Deploy, configure, and operationalize agentic AI workflows, AI assistants, and AI-powered automations within client ServiceNow environments and enterprise technology ecosystems.
• Translate customer business requirements, operational processes, and desired outcomes into technical architectures, implementation plans, and production-ready AI solutions.
• Implement and integrate NewRocket Agent Packs, AI accelerators, and workflow solutions into enterprise environments.
• Work directly with customer teams to tailor AI solutions to their operating models, business processes, data sources, security requirements, and user needs.
• Support workshops, discovery sessions, technical working sessions, demonstrations, pilots, and production rollouts.
• Clearly communicate AI capabilities, limitations, tradeoffs, solution behavior, and adoption considerations to technical and business stakeholders.

Anthropic and Generative AI Engineering

• Build enterprise AI applications and workflows using Claude, the Anthropic API, and other LLM platforms as appropriate for the client use case.
• Apply effective prompt and context-engineering techniques, including clear instructions, examples, role and task definition, structured inputs, response constraints, and long-context management.
• Design and implement AI workflows using structured outputs, tool use/function calling, API integrations, multi-step orchestration, and human-in-the-loop review patterns.
• Build retrieval-augmented generation (RAG) solutions that ground AI responses in authorized enterprise data and knowledge sources.
• Implement practical techniques to improve reliability and user trust, including citation or source-grounding patterns, validation, confidence thresholds, output schemas, fallback handling, and escalation workflows.
• Stay current on Anthropic platform capabilities, Claude model releases, implementation guidance, responsible AI principles, and enterprise deployment best practices.
• Complete relevant Anthropic training, partner enablement, and technical education programs as available, and incorporate those practices into NewRocket solution delivery.

Solution Engineering & Prototyping

• Build demos, prototypes, and proof-of-concept implementations that validate AI-driven workflows and customer use cases.
• Rapidly iterate with customers and internal teams to refine AI-powered solutions based on feedback, performance results, and operational needs.
• Support the design and implementation of AI orchestration, LLM integrations, agentic decision models, and workflow automation patterns.
• Evaluate when an agentic approach is appropriate versus deterministic automation, traditional workflow logic, search, analytics, or human review.
• Develop reusable implementation patterns, solution templates, prompt libraries, integrations, and deployment assets that accelerate future client delivery.

Full-Stack Engineering & Integration

• Develop secure integrations between ServiceNow, enterprise systems, APIs, data platforms, and AI services.
• Build supporting components such as scripts, microservices, automation logic, integration services, and lightweight user interfaces.
• Implement integrations with AI platforms, enterprise APIs, identity systems, document repositories, databases, and structured and unstructured data sources.
• Apply sound engineering practices for authentication, authorization, secrets management, access controls, logging, error handling, version control, and documentation.
• Design solutions that meet enterprise expectations for security, scalability, maintainability, observability, and production readiness.

AI Quality, Evaluation & Responsible AI

• Develop and execute practical evaluation approaches for AI applications, including test cases, representative datasets, success metrics, and regression testing.
• Assess AI workflow quality across dimensions such as relevance, accuracy, groundedness, task completion, safety, latency, cost, and user experience.
• Implement safeguards for sensitive data, role-based permissions, appropriate data access, prompt injection risks, unsafe tool use, and unintended model behavior.
• Establish human-in-the-loop workflows for sensitive, high-impact, low-confidence, or exception-based decisions.
• Document AI solution behavior, known limitations, risk controls, governance considerations, and operational support procedures.
• Monitor and improve deployed solutions based on user feedback, usage patterns, performance data, incidents, and evolving customer needs.

Product & Platform Contribution

Actively contribute to the development and evolution of NewRocket's AI intellectual property and platforms, including:

• NewRocket Intelligence Platform
• Value Realization Dashboard
• Data Intelligence Platform
• Agent Packs and reusable AI solution accelerators

Responsibilities include:

• Identifying common patterns, requirements, integration needs, and capabilities discovered through customer deployments.
• Contributing reusable assets, integration components, prompt patterns, evaluation frameworks, and automation capabilities.
• Providing actionable product feedback that improves usability, reliability, scalability, security, and customer value.
• Helping transform successful client implementations into repeatable platform features, accelerators, and delivery playbooks.
• Supporting the definition of standards and best practices for enterprise AI delivery across NewRocket's AI Foundry.

Systems Integration & Troubleshooting

• Diagnose and resolve technical issues across AI workflows, integrations, retrieval pipelines, data connections, and automation processes.
• Troubleshoot issues related to model inputs and outputs, prompt behavior, tool execution, API reliability, permissions, data quality, and system performance.
• Ensure deployed AI solutions are secure, scalable, supportable, and production-ready.
• Optimize deployed systems for reliability, performance, latency, model usage, and cost efficiency.

Cross-Team Collaboration

• Work closely with Business Process Consultants, Product Engineering, Data Engineers, AI/ML Engineers, ServiceNow teams, and the AI Center of Excellence.
• Serve as the engineering counterpart to consulting and delivery teams throughout discovery, solution design, implementation, rollout, and continuous improvement.
• Contribute to internal playbooks, technical documentation, reusable deployment patterns, reference architectures, and product evolution.
• Share lessons learned from customer deployments to strengthen NewRocket's AI delivery capabilities and solution portfolio.

What Success Looks Like in the First 6 Months

• Successfully deploy AI-powered workflows, assistants, automations, or integrations across multiple customer engagements.
• Deliver secure, reliable AI workflows within customer ServiceNow environments and connected enterprise systems.
• Build trusted relationships with customer technical teams, business stakeholders, and NewRocket delivery teams.
• Demonstrate strong practical application of Claude and modern LLM engineering practices, including prompt/context engineering, tool use, RAG, evaluations, and responsible AI controls.
• Contribute reusable components, implementation patterns, prompt assets, and improvements to NewRocket's AI platforms and accelerators.
• Provide actionable customer-driven feedback that improves the NewRocket Intelligence Platform and related products.
• Help establish repeatable methods for moving AI use cases from prototype through governed production deployment.

Required Qualifications

• 5-8 years of experience in software engineering, systems integration, enterprise platforms, workflow automation, or related technical delivery roles.
• Strong engineering foundation, with 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, AI-enabled automations, conversational experiences, RAG systems, or agentic workflows.
• Experience integrating APIs, enterprise applications, data platforms, or workflow systems in production environments.
• Proficiency in JavaScript/TypeScript, Python, or similar programming and scripting languages.
• Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
• Familiarity with modern LLM application concepts, including prompt engineering, context windows, token usage, embeddings, vector search, RAG, tool use/function calling, structured outputs, and model evaluation.
• Understanding of responsible AI concepts, including hallucination mitigation, sensitive-data handling, identity and access controls, human oversight, AI safety, and secure AI deployment.
• Experience operating in customer-facing engineering, consulting, technical implementation, solutions architecture, or professional-services roles.
• Strong problem-solving skills and the ability to communicate complex technical concepts clearly to both technical and business stakeholders.
• Ability to manage ambiguity, prioritize effectively, travel approximately 25%, and deliver high-quality solutions in fast-moving customer environments.

Preferred Qualifications

Anthropic / Claude Experience

• Hands-on experience with the Anthropic API, Claude models, Anthropic Console, Claude Code, or Anthropic implementation guidance.
• Completion of relevant Anthropic Academy learning, partner enablement, technical training, or equivalent hands-on experience deploying Claude-based solutions.
• Experience applying Claude capabilities such as long-context processing, tool use, structured outputs, document analysis, and enterprise knowledge workflows.
• Familiarity with Model Context Protocol (MCP) concepts and experience building or integrating secure tools and data connections for AI applications.

ServiceNow Experience - Strong Plus

• Experience with ServiceNow development, configuration, workflow automation, or enterprise platform implementation.
• Familiarity with ServiceNow scripting, APIs, IntegrationHub, Flow Designer, Virtual Agent, Now Assist, AI Agents, and platform development patterns.
• Understanding of ServiceNow data models and enterprise workflow concepts, including:
• CMDB
• ITSM, CSM, HRSD, or employee workflows
• Workflow and task tables
• Knowledge management
• Enterprise integrations and data exchange patterns

AI, Data & Platform Experience

• Experience building AI prototypes, RAG systems, semantic search capabilities, AI assistants, or agent-based workflows.
• Experience integrating LLM APIs or AI services into enterprise applications and business processes.
• Experience with vector databases, embeddings, document ingestion, chunking, retrieval strategies, and knowledge-grounding patterns.
• Familiarity with LLM orchestration and application frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or comparable tools.
• Experience with AI observability, tracing, evaluation frameworks, prompt/version management, monitoring, and cost optimization.
• Experience contributing to internal platforms, reusable accelerators, developer tooling, or product capabilities.
• Familiarity with Docker, CI/CD, SQL, Git, infrastructure-as-code, and secure cloud deployment practices.