Forward Deployed AI Engineer/Anthropic – Data Intelligence-US West

About the position NewRocket is seeking a hands-on, client-facing Forward Deployed AI Engineer with a solid foundation in Data Intelligence to design, build, test, and deploy enterprise AI solutions grounded in high-quality, governed enterprise data. This role sits at the intersection of AI engineering, data engineering, enterprise integration, and consulting. You will work directly with client stakeholders and NewRocket delivery teams to translate business challenges into production-ready solutions that connect Claude and other AI technologies with ServiceNow, enterprise knowledge, structured data, business processes, and approved tools. The Forward Deployed AI Engineer – Data Intelligence will focus especially on the data foundations required for trustworthy AI: data discovery, ingestion, transformation, quality, metadata, access controls, retrieval, semantic search, retrieval-augmented generation (RAG), evaluation, observability, and ongoing optimization. You will help clients move beyond disconnected data and experimental AI pilots to scalable solutions that enable more intelligent workflows, improved decision-making, and measurable operational value. The ideal candidate is an adaptable engineer with strong Python, APIs, data, cloud, and LLM application-development skills. You are comfortable working in ambiguous environments, collaborating directly with customers, and balancing rapid prototyping with the rigor required for secure enterprise production deployments. Responsibilities Partner directly with client business, data, technology, security, and ServiceNow stakeholders to identify high-value AI and Data Intelligence use cases.Translate client requirements into practical technical designs, prototypes, production implementations, and iterative delivery plans.Build AI-enabled applications and workflows that use trusted enterprise data to support knowledge discovery, employee assistance, service operations, customer service, document intelligence, decision support, and workflow automation.Develop reusable Data Intelligence components, accelerators, integration patterns, and implementation playbooks that can be applied across client engagements.Support the full solution lifecycle—from discovery, data assessment, and proof of concept through implementation, testing, production rollout, monitoring, and continuous improvement.Communicate solution designs, technical tradeoffs, risks, findings, and recommendations clearly to technical and non-technical client stakeholders.Design and implement pipelines to ingest, transform, enrich, index, and retrieve structured and unstructured enterprise data.Connect AI solutions to approved enterprise data sources, including ServiceNow, knowledge bases, document repositories, collaboration platforms, databases, data warehouses, data lakes, and third-party SaaS systems.Support data profiling, data-quality assessment, schema mapping, metadata enrichment, classification, normalization, deduplication, and data lineage activities.Work with client data owners and governance teams to define appropriate data access, retention, privacy, security, and usage controls.Build data integration workflows using APIs, SQL, ETL/ELT tools, event-driven patterns, middleware, and custom services as appropriate.Help establish trusted-data patterns that ensure AI applications retrieve current, relevant, authorized, and contextually appropriate information.Identify data gaps, quality issues, duplicate content, stale information, and access-control problems that may reduce AI solution performance or user trust.Design, build, and optimize retrieval-augmented generation (RAG) solutions using Claude and other approved LLM technologies.Implement document-processing and knowledge-ingestion workflows, including parsing, chunking, metadata enrichment, embeddings, indexing, vector storage, hybrid retrieval, reranking, and source attribution.Develop semantic-search and enterprise knowledge experiences that help users discover, understand, summarize, and act on information.Configure and evaluate vector databases, search platforms, relational databases, and enterprise knowledge repositories appropriate to the client's environment.Build access-aware retrieval patterns that respect source-system permissions and ensure users only receive information they are authorized to access.Improve answer quality and reliability through retrieval tuning, context management, source citation, grounding, relevance scoring, fallback behavior, and user feedback loops.Define and execute RAG evaluations measuring retrieval quality, context relevance, groundedness, completeness, accuracy, latency, cost, and user experience.Build and deploy LLM-powered applications using Claude, the Anthropic API, and other approved model providers as appropriate.Develop prompt and context-engineering approaches that use clear instructions, structured inputs, examples, retrieval context, output schemas, and guardrails.Implement structured outpu