Senior/Lead Forward Deployed AI Engineer/Anthropic – Data Intelligence-US East

Role

NewRocket is hiring a hands-on, client-facing Forward Deployed AI Engineer (Senior/Lead) for Data Intelligence to design, build, test, and deploy enterprise AI solutions grounded in governed enterprise data. The role sits at the intersection of AI engineering, data engineering, enterprise integration, and consulting, focusing on trustworthy data foundations and production-ready LLM applications (Claude and other approved models).

Key Responsibilities

Data Intelligence & AI Solution Delivery

• Partner with client business, data, technology, security, and ServiceNow stakeholders to identify high-value use cases.
• Translate requirements into technical designs, prototypes, and production implementations.
• Build AI-enabled applications and workflows for knowledge discovery, service operations, document intelligence, decision support, and automation.
• Develop reusable Data Intelligence components, accelerators, integration patterns, and playbooks.
• Support full solution lifecycle: discovery, PoC, implementation, testing, rollout, monitoring, and continuous improvement.
• Communicate technical tradeoffs, risks, and recommendations to technical and non-technical stakeholders.

Enterprise Data Foundations

• Design and implement pipelines to ingest, transform, enrich, index, and retrieve structured and unstructured data.
• Connect AI solutions to ServiceNow, knowledge bases, document repositories, collaboration platforms, databases, data warehouses, lakes, and third-party SaaS.
• Support data profiling, quality assessment, schema mapping, 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, metadata enrichment, 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 implement retrieval tuning, citation, grounding, and feedback loops.

Claude, LLM & Agentic AI Development

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

ServiceNow & Enterprise Workflow Integration

• Integrate AI and Data Intelligence capabilities with ServiceNow workflows, data, knowledge, APIs, and UX.
• Collaborate with ServiceNow architects and developers to meet security, scalability, and maintainability standards.

Evaluation, Observability & Continuous Improvement

• Develop test plans, evaluation datasets, and QA processes for data-intensive AI solutions.
• Measure and improve performance across data quality, retrieval quality, model output quality, latency, reliability, adoption, and cost.
• Implement logging, tracing, monitoring, and feedback across pipelines, retrieval systems, model calls, and integrations.
• Contribute to LLMOps and DataOps practices for reliable deployment and governance.

Responsible AI, Security & Governance

• Apply responsible-AI, security, privacy, and governance controls throughout design and deployment.
• Implement safeguards for sensitive data, prompt injection, unauthorized access, output validation, and audit logging.
• Work with security, governance, legal, compliance, and risk stakeholders to align with enterprise policies and regulations.

What Success Looks Like in the First 6 Months

• Build trusted client and internal relationships and deliver one or more Data Intelligence/AI solutions from prototype to production readiness.
• Establish or improve secure data ingestion, retrieval, RAG, and enterprise integration capabilities for client engagements.
• Improve AI reliability via data preparation, access-aware retrieval, prompt/context engineering, testing, evaluation, and observability.
• Contribute reusable code, architecture patterns, accelerators, and delivery playbooks.
• Demonstrate measurable improvements in solution quality, user experience, efficiency, adoption, or business outcomes.

Requirements

Required

• 3+ years of relevant experience in software engineering, AI engineering, data engineering, analytics engineering, cloud engineering, systems integration, or related technical roles.
• Hands-on experience building applications, data pipelines, integrations, APIs, automations, or cloud services.
• Strong proficiency in Python; JavaScript/TypeScript, Java, and SQL are valuable.
• Experience with structured and unstructured data, relational databases, document repositories, APIs, and cloud storage.
• Experience with SQL, data transformation, 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 AWS, Microsoft Azure, or Google Cloud Platform.
• Knowledge of software-development best practices: Git, code review, testing, debugging, documentation, and 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 RAG systems, document ingestion, embeddings, vector DBs, hybrid search, reranking, citations, and retrieval evaluation.
• Experience with vector DBs/search tech: Pinecone, Weaviate, pgvector, OpenSearch, Elasticsearch, Azure AI Search, Vertex AI Search.
• Experience with LLM frameworks: LangChain, LangGraph, LlamaIndex, Semantic Kernel, or equivalents.
• Experience with agentic AI, function calling, workflow orchestration, MCP, or secure AI-system integrations.
• Experience with data platforms: Snowflake, Databricks, BigQuery, Redshift, PostgreSQL, MongoDB, Microsoft Fabric.
• Familiarity with ServiceNow development/architecture (APIs, IntegrationHub, Flow Designer, Virtual Agent, Now Assist, AI Agents, CMDB, ITSM/CSM/HRSD).
• Experience with Docker, Kubernetes, Terraform, CI/CD, cloud-native services, orchestration, monitoring, and observability.
• Enterprise consulting/professional services experience and relevant certifications.

Notes

• Role may require travel based on client and business needs.
• Position emphasizes enterprise-grade security, governance, and measurable outcomes.