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

Role

Forward Deployed AI Engineer — Data Intelligence responsible for designing, building, testing, and deploying enterprise AI solutions grounded in governed, high-quality enterprise data. This client-facing role blends AI engineering, data engineering, enterprise integration, and consulting to connect Claude and other LLM technologies with ServiceNow, knowledge sources, structured data, and business workflows.

Location & Travel

Location: [Location / Hybrid / Remote] (flexible/unspecified). Travel: up to 25–50% depending on client and business needs.

Key Responsibilities

• Partner with client business, data, technology, 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 for knowledge discovery, employee assistance, service operations, document intelligence, and workflow automation.
• Design and implement data pipelines for ingestion, transformation, enrichment, indexing, and retrieval of structured and unstructured data.
• Implement RAG solutions, semantic search, embeddings, document processing (parsing, chunking, metadata enrichment), indexing, and vector storage.
• Develop prompt and context-engineering approaches, structured outputs, tool use/function calling, and agentic workflows with human-in-the-loop controls.
• Integrate AI capabilities with ServiceNow APIs, workflows, data, and user experiences.
• Implement evaluation, observability, logging, monitoring, and feedback loops across pipelines, retrieval systems, model calls, and agent workflows.
• Apply responsible AI, security, privacy, and governance controls across design, development, testing, and deployment.
• Contribute reusable components, accelerators, playbooks, and practice-level improvements for Data Intelligence delivery.

Requirements

• 3+ years relevant engineering experience (software, AI, data, analytics, cloud, or systems integration).
• Hands-on experience building applications, data pipelines, integrations, APIs, automations, or cloud services.
• Strong proficiency in Python; experience with JavaScript/TypeScript, Java, and SQL 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 building API-driven integrations using REST, JSON, OAuth, service accounts, and authentication/authorization patterns.
• Familiarity with cloud platforms (AWS, Azure, GCP).
• Understanding of software development best practices: Git, code review, testing, debugging, documentation, and agile delivery.
• Strong problem-solving and communication skills; ability to work directly with clients in consulting/professional-services engagements.

Preferred Qualifications

• Experience with Claude, Anthropic API/Console/Code or Anthropic enablement resources.
• Hands-on experience designing or implementing RAG systems and using vector databases and search technologies (e.g., Pinecone, Weaviate, pgvector, OpenSearch, Elasticsearch, Azure AI Search, Vertex AI Search).
• Familiarity with LLM application frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel.
• Experience with agentic AI, function calling, workflow orchestration, and Model Context Protocol (MCP) or secure AI-to-system integrations.
• Experience with data platforms (Snowflake, Databricks, BigQuery, Redshift, PostgreSQL, MongoDB, Microsoft Fabric).
• Experience with Docker, Kubernetes, Terraform, CI/CD, data orchestration, monitoring, and observability tools.
• Experience in enterprise consulting or professional services and relevant certifications.

Success in the First 6 Months

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

Notes

• Emphasis on secure, governed, and production-ready AI solutions connecting LLMs (Claude/Anthropic and others) with enterprise data and ServiceNow.
• Role includes significant client-facing and consulting responsibilities and may require travel.