Forward Deployed AI Engineer/Anthropic - Data Intelligence

AI Foundry | NewRocket
Location: [Location / Hybrid / Remote]
Travel: Up to [25-50%], based on client and business needs
Reports to: AI Delivery Leader

Role Overview

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.

Key Responsibilities

Data Intelligence & AI Solution Delivery

• 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.

Enterprise Data Foundations

• 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.

RAG, Search & Enterprise Knowledge Engineering

• 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.

Claude, LLM & Agentic AI Development

• 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 outputs, tool use/function calling, API integrations, workflow orchestration, and error-handling patterns for reliable AI applications.
• Build agentic AI workflows that can reason over approved data, access authorized tools, execute bounded tasks, and route exceptions to human reviewers.
• Define agent instructions, context strategies, tool permissions, validation logic, escalation paths, and human-in-the-loop controls.
• Support secure Model Context Protocol (MCP) or comparable patterns for connecting AI applications to authorized enterprise systems and tools.
• Evaluate AI and agentic workflow behavior for task completion, consistency, safety, accuracy, groundedness, latency, cost, and operational reliability.

ServiceNow & Enterprise Workflow Integration

• Integrate AI and Data Intelligence capabilities with ServiceNow workflows, data, knowledge, APIs, and user experiences.
• Collaborate with ServiceNow architects and developers to ensure AI solutions follow platform leading practices, security requirements, scalability expectations, and maintainability standards.
• Help clients embed AI insights and recommendations into the workflows where employees and customers already work.

Evaluation, Observability & Continuous Improvement

• Develop test plans, test cases, evaluation datasets, and quality-assurance processes for AI and data-intensive solutions.
• Measure and improve solution performance across data quality, retrieval quality, model output quality, task completion, latency, reliability, adoption, and cost.
• Implement logging, tracing, monitoring, and feedback mechanisms across data pipelines, retrieval systems, model calls, agent workflows, and integrations.
• Investigate production issues, identify root causes, document findings, and implement durable improvements.
• Support release-management practices, including version control for code, prompts, configuration, evaluation assets, data pipelines, and infrastructure.
• Contribute to LLMOps and DataOps practices that enable reliable deployment, testing, monitoring, governance, and ongoing optimization.

Responsible AI, Security & Governance

• Apply responsible-AI, security, privacy, and governance requirements throughout the design, development, testing, and deployment lifecycle.
• Implement safeguards for sensitive data, data leakage, unauthorized access, prompt injection, malicious content, unsafe tool use, and unintended agent behavior.
• Support controls such as access-aware retrieval, data masking, encryption, output validation, source attribution, approval workflows, audit logging, and confidence-based escalation.
• Work with client security, data governance, legal, compliance, and risk stakeholders to align solutions with enterprise policies and regulatory requirements.
• Document technical designs, data flows, security controls, model limitations, evaluation results, operating procedures, and known risks.

Collaboration, Innovation & Practice Development

• Work closely with AI Architects, AI Platform Engineers, data engineers, ServiceNow developers, product managers, designers, consultants, and client teams.
• Participate in discovery workshops, architecture sessions, sprint planning, backlog refinement, demos, code reviews, retrospectives, and executive readouts.
• Support client-facing technical research, demos, proofs of concept, implementation planning, and solution presentations.
• Contribute reusable code, Data Intelligence patterns, RAG components, evaluation assets, technical playbooks, and internal documentation.
• Stay current on Anthropic and Claude capabilities, enterprise AI trends, data platforms, RAG frameworks, semantic search, vector databases, agentic AI, and ServiceNow AI innovations.
• Identify opportunities to improve NewRocket's Data Intelligence offerings, AI Foundry accelerators, Agent Packs, and enterprise AI delivery methodology.

What Success Looks Like in the First 6 Months

• Build trusted relationships with client stakeholders and NewRocket AI Foundry delivery teams.
• Deliver one or more high-quality, client-facing Data Intelligence or AI solutions from prototype through production-ready implementation.
• Establish or enhance secure data-ingestion, retrieval, RAG, and enterprise integration capabilities for assigned client engagements.
• Improve AI reliability through thoughtful data preparation, access-aware retrieval, prompt/context engineering, testing, evaluation, and observability.
• Help clients connect Claude-powered AI solutions to ServiceNow, enterprise knowledge, structured data, and operational workflows.
• Contribute reusable code, architecture patterns, accelerators, and delivery playbooks to NewRocket's Data Intelligence and Anthropic business.
• Demonstrate measurable improvements in solution quality, user experience, efficiency, adoption, or business outcomes.

Required Qualifications

• 3+ years of relevant experience in software engineering, AI engineering, data engineering, analytics engineering, cloud engineering, systems integration, or a related technical role.
• 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 also valuable.
• Experience working with structured and unstructured data, including relational databases, document repositories, APIs, and cloud storage.
• Experience with SQL, data transformation, data modeling, ETL/ELT, data ingestion, or data-integration concepts.
• Exposure to generative AI, LLMs, RAG, embeddings, vector search, semantic search, prompt engineering, AI agents, or LLM APIs.
• Experience building or supporting API-driven integrations using REST APIs, JSON, OAuth, service accounts, and authentication/authorization patterns.
• Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
• Understanding of software-development best practices, including Git, code review, testing, debugging, documentation, and agile delivery.
• Strong problem-solving skills and the ability to work through ambiguity in client environments.
• Strong written and verbal communication skills, including the ability to explain technical concepts to non-technical stakeholders.
• Ability and willingness to work directly with clients in a consulting and professional-services environment.

Preferred Qualifications

• Experience with Claude, the Anthropic API, Anthropic Console, Claude Code, Anthropic Academy learning, or Anthropic partner enablement.
• Experience designing or implementing RAG systems, including document ingestion, chunking, embeddings, vector databases, hybrid search, reranking, citations, and retrieval evaluation.
• Experience with vector databases or search technologies such as Pinecone, Weaviate, pgvector, OpenSearch, Elasticsearch, Azure AI Search, Vertex AI Search, or similar tools.
• Experience using LLM application frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or equivalent technologies.
• Experience with agentic AI, tool use, functio