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

Role

NewRocket is hiring a hands-on, client-facing Forward Deployed AI Engineer focused on Data Intelligence to design, build, test, and deploy enterprise AI solutions that integrate Claude and other LLMs with governed enterprise data and ServiceNow. This role combines AI engineering, data engineering, enterprise integration, and consulting and supports clients from discovery through production and continuous improvement.

Key Responsibilities

Data Intelligence & AI Solution Delivery

• Partner with client business, data, security, and ServiceNow stakeholders to identify high-value AI and Data Intelligence use cases.
• Translate requirements into technical designs, prototypes, production implementations, and iterative delivery plans.
• Build AI-enabled applications and workflows that leverage trusted enterprise data for knowledge discovery, employee assistance, service operations, document intelligence, decision support, and automation.
• Develop reusable Data Intelligence components, accelerators, and integration patterns.
• Support full lifecycle delivery from discovery and PoC to rollout, monitoring, and optimization.

Enterprise Data Foundations

• Design and implement pipelines to ingest, transform, enrich, index, and retrieve structured and unstructured enterprise data.
• Connect AI solutions to enterprise sources (ServiceNow, knowledge bases, document repositories, databases, data lakes, SaaS systems).
• Support data profiling, quality assessment, metadata enrichment, classification, normalization, deduplication, and lineage.
• Define data access, retention, privacy, security, and usage controls with governance teams.
• Build integrations using APIs, SQL, ETL/ELT tools, event-driven patterns, middleware, and custom services.

RAG, Search & Enterprise Knowledge Engineering

• Design, build, and optimize retrieval-augmented generation (RAG) solutions using Claude and other LLMs.
• Implement document processing, chunking, embeddings, indexing, vector storage, hybrid retrieval, reranking, and source attribution.
• Configure and evaluate vector databases, search platforms, relational databases, and knowledge repositories.
• Build access-aware retrieval patterns respecting source permissions and improve answer quality via retrieval tuning, grounding, and feedback loops.

Claude, LLM & Agentic AI Development

• Build and deploy LLM-powered applications using Claude, the Anthropic API, and other approved model providers.
• Apply prompt and context engineering, structured outputs, tool use/function calling, workflow orchestration, and error handling.
• Develop agentic AI workflows with bounded tool access, validation logic, escalation paths, and human-in-the-loop controls.
• Support Model Context Protocol (MCP) or comparable secure connection patterns between AI applications and enterprise systems.

ServiceNow & Enterprise Workflow Integration

• Integrate AI and Data Intelligence capabilities with ServiceNow workflows, APIs, and user experiences.
• Collaborate with ServiceNow architects to ensure security, scalability, and maintainability.

Evaluation, Observability & Continuous Improvement

• Develop test plans, evaluation datasets, and QA processes for AI and data-intensive solutions.
• Measure and improve performance across data quality, retrieval quality, model output quality, latency, reliability, adoption, and cost.
• Implement logging, tracing, monitoring, and feedback systems across pipelines, retrieval systems, model calls, and agent workflows.

Responsible AI, Security & Governance

• Apply responsible-AI, security, privacy, and governance requirements across design, development, testing, and deployment.
• Implement safeguards for sensitive data, data masking, encryption, output validation, source attribution, audit logging, and confidence-based escalation.

What Success Looks Like in the First 6 Months

• Build trusted client and internal relationships and deliver one or more Data Intelligence or AI solutions from prototype to production.
• Establish or enhance secure data-ingestion, retrieval, RAG, and enterprise integration capabilities.
• Improve AI reliability through data preparation, access-aware retrieval, prompt/context engineering, testing, and observability.
• Contribute reusable code, architecture patterns, accelerators, and delivery playbooks.

Requirements

Required

• 3+ years of relevant experience in software, AI, data, analytics, cloud engineering, systems integration, or related roles.
• Hands-on experience building applications, data pipelines, integrations, APIs, automations, or cloud-based services.
• Strong proficiency in Python; experience with JavaScript/TypeScript, Java, SQL, or similar languages is valuable.
• Experience with structured and unstructured data, relational databases, document repositories, APIs, and cloud storage.
• Experience with SQL, data transformation, data modeling, ETL/ELT, and data-integration concepts.
• Exposure to generative AI, LLMs, RAG, embeddings, vector search, semantic search, prompt engineering, AI agents, or LLM APIs.
• Experience with REST APIs, JSON, OAuth, service accounts, and authentication/authorization patterns.
• Familiarity with cloud platforms (AWS, Azure, or GCP).
• Understanding of software-development best practices (Git, code review, testing, debugging, documentation, agile delivery).
• Strong problem-solving, communication, and client-facing consulting skills.

Preferred

• Experience with Claude, Anthropic API/Console, Claude Code, or Anthropic enablement.
• Experience designing/implementing RAG systems, embeddings, vector DBs, hybrid search, reranking, citations, and retrieval evaluation.
• Familiarity with vector databases/search technologies (Pinecone, Weaviate, pgvector, OpenSearch, Elasticsearch, Azure AI Search, Vertex AI Search).
• Experience with LLM application frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel).
• Experience with agentic AI, tool use, function calling, workflow orchestration, MCP, and secure AI-to-system integrations.
• Experience with Snowflake, Databricks, BigQuery, Redshift, PostgreSQL, MongoDB, Microsoft Fabric, or similar.
• Familiarity with ServiceNow development/architecture and related modules (IntegrationHub, Flow Designer, Virtual Agent, Now Assist, AI Agents, CMDB, ITSM, CSM, HRSD).
• Experience with Docker, Kubernetes, Terraform, CI/CD, cloud-native services, data orchestration, monitoring, or observability tools.
• Experience in enterprise consulting or professional services and relevant certifications.

Other Details

• Location: [Location / Hybrid / Remote] (client-facing; travel up to 25–50% depending on client/business needs).
• Reports to: AI Delivery Leader

Why This Role Matters

This role builds the data foundations enabling Claude-powered applications and enterprise AI to move from experimentation to secure, scalable, measurable outcomes by making enterprise data discoverable, governed, and actionable.