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

Summary

NewRocket is an AI-first Elite ServiceNow Partner that helps enterprises adopt trusted AI and realize value on the Now Platform. The Forward Deployed AI Engineer will design, build, test, and deploy client-facing AI solutions grounded in governed enterprise data, with responsibilities spanning data pipelines, RAG, Claude and LLM applications, ServiceNow integration, evaluation, observability, security, and governance.

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 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
• 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
• 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
• 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
• 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

Skills

• 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
• 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, function calling, workflow orchestration, Model Context Protocol (MCP), or secure AI-to-system integrations
• Experience with data platforms such as Snowflake, Databricks, BigQuery, Redshift, PostgreSQL, MongoDB, Microsoft Fabric, or similar technologies
• Experience with data cataloging, governance, lineage, data-quality tooling, master data management, or enterprise metadata practices
• Familiarity with ServiceNow development or architecture, including ServiceNow APIs, IntegrationHub, Flow Designer, Virtual Agent, Now Assist, AI Agents, knowledge management, CMDB, ITSM, CSM, or HRSD
• Experience with Docker, Kubernetes, Terraform, CI/CD, cloud-native services, data orchestration, monitoring, or observability tools
• Experience working in enterprise consulting, professional services, or client-facing technology-delivery roles
• Relevant cloud, data, ServiceNow, AI/ML, security, or agile certifications

Qualifications

Must Haves

• 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

Nice to Haves

• 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, function calling, workflow orchestration, Model Context Protocol (MCP), or secure AI-to-system integrations
• Experience with data platforms such as Snowflake, Databricks, BigQuery, Redshift, PostgreSQL, MongoDB, Microsoft Fabric, or similar technologies
• Experience with data cataloging, governance, lineage, data-quality tooling, master data management, or enterprise metadata practices
• Familiarity with ServiceNow development or architecture, including ServiceNow APIs, IntegrationHub, Flow Designer, Virtual Agent, Now Assist, AI Agents, knowledge management, CMDB, ITSM, CSM, or HRSD
• Experience with Docker, Kubernetes, Terraform, CI/CD, cloud-native services, data orchestration, monitoring, or observability tools
• Experience working in enterprise consulting, professional services, or client-facing technology-delivery roles
• Relevant cloud, data, ServiceNow, AI/ML, security, or agile certifications