AI Platform Engineer-Anthropic-US East

AI Platform Engineer-Anthropic

AI Foundry | NewRocket
Location: [Location / Hybrid / Remote]
Travel based on client and business needs
Reports to: Global AI Center of Excellence Lead / AI Platform Architect

About NewRocket

NewRocket is the AI-first Elite ServiceNow Partner that activates real value on the Now Platform. As a trusted advisor to enterprise leaders, we combine industry expertise, human-centered design, and enterprise-grade AI to help organizations navigate change and scale with confidence.

With two decades of experience guiding clients to realize the full potential of the ServiceNow AI Platform, NewRocket is one of the largest pure-play ServiceNow partners. We are uniquely focused on enabling enterprises to adopt AI they trust—AI that delivers lasting business value.

NewRocket is proud to be an Anthropic partner/vendor. Through this relationship, we are expanding our ability to help enterprise clients responsibly design, deploy, and scale AI solutions powered by Claude and other leading AI technologies. Our AI Foundry teams apply Anthropic-aligned practices across prompt and context engineering, retrieval-augmented generation (RAG), agentic workflows, tool use, structured outputs, model evaluation, security, governance, and human-in-the-loop controls.

We #GoBeyondWorkflows to create new kinds of experiences for our customers.

Come join our Crew!

Role Overview

NewRocket is seeking an experienced AI Platform Engineer to build, operate, and continuously improve the technical foundations that enable secure, reliable, scalable enterprise AI solutions.

This role combines cloud engineering, platform engineering, DevOps, MLOps/LLMOps, data-platform integration, and applied AI engineering. The AI Platform Engineer will work closely with AI Architects, Forward Deployed AI Engineers, data engineers, ServiceNow teams, product engineering, and customer stakeholders to create reusable platforms, deployment patterns, controls, and operational capabilities for NewRocket’s Anthropic and enterprise AI business.

You will help establish the infrastructure and engineering practices required to move AI solutions from prototype to governed production use. This includes enabling Claude and other LLM-powered applications; supporting RAG and agentic workflows; integrating enterprise data and tools; implementing observability and evaluation; and maintaining strong security, privacy, and governance controls.

The ideal candidate is a hands-on engineer who is comfortable working across cloud infrastructure, APIs, CI/CD, data systems, containers, AI application frameworks, and enterprise security requirements. You are equally motivated by building reusable internal capabilities and solving practical customer-delivery challenges.

Key Responsibilities

AI Platform Architecture & Engineering

• Design, build, deploy, and maintain scalable platform capabilities that support enterprise AI, machine learning, LLM, RAG, and agentic AI applications.
• Create reusable reference architectures, infrastructure patterns, deployment templates, integration components, and engineering standards for NewRocket’s AI Foundry.
• Build platform capabilities that enable AI applications to securely connect to enterprise data, APIs, workflow systems, and authorized tools.
• Partner with AI Architects and Forward Deployed AI Engineers to translate client needs into reliable, supportable technical platform designs.
• Support the technical evolution of NewRocket’s AI intellectual property, including the NewRocket Intelligence Platform, Data Intelligence Platform, Value Realization Dashboard, Agent Packs, and reusable AI accelerators.
• Evaluate and recommend cloud, data, AI, observability, orchestration, and security technologies that improve delivery speed, quality, scalability, and cost efficiency.

Anthropic, Claude & LLM Platform Enablement

• Build and maintain secure, reusable integrations with the Anthropic API, Claude models, and other approved AI services.
• Enable LLM-powered applications through standardized patterns for authentication, model access, prompt and context management, structured outputs, tool use, logging, error handling, and rate-limit management.
• Support Claude-based enterprise use cases involving document analysis, knowledge assistance, workflow automation, agentic task execution, summarization, classification, and decision support.
• Develop technical patterns for long-context workflows, document processing, RAG, structured data extraction, and model-driven automation.
• Support secure Model Context Protocol (MCP) and comparable tool-integration patterns that allow AI applications to access approved enterprise systems and data safely.
• Stay current on Anthropic platform capabilities, product releases, security guidance, technical enablement, and responsible AI practices.
• Complete relevant Anthropic partner training and enablement as available and help translate learning into reusable NewRocket engineering standards.

LLMOps, MLOps & AI Operations

• Establish and operate CI/CD pipelines for AI applications, model configurations, prompts, evaluation assets, infrastructure, and integration services.
• Implement versioning, testing, release-management, rollback, and change-control practices for AI solutions.
• Build and maintain LLMOps and MLOps capabilities, including model/prompt configuration management, evaluation pipelines, deployment automation, monitoring, and lifecycle management.
• Develop automated evaluation and regression-testing frameworks to measure AI quality before and after releases.
• Support production operations for AI services, including incident response, troubleshooting, root-cause analysis, capacity planning, and service-level monitoring.
• Define and monitor operational metrics such as availability, latency, throughput, token consumption, model cost, tool-call success rates, task-completion rates, and error rates.
• Improve platform reliability, performance, resilience, and cost efficiency through automation, tuning, and operational improvements.

Cloud Infrastructure, DevOps & Security

• Design and manage cloud infrastructure across AWS, Microsoft Azure, Google Cloud Platform, or client-approved environments.
• Build and maintain infrastructure using infrastructure-as-code tools such as Terraform, CloudFormation, Bicep, Pulumi, or comparable technologies.
• Implement containerized application and AI-service deployments using Docker, Kubernetes, serverless services, and cloud-native application patterns.
• Develop secure CI/CD workflows using Git-based source control, automated testing, artifact management, secrets management, and policy controls.
• Implement identity, access, and authentication patterns, including role-based access control, least-privilege access, API security, service accounts, and credential rotation.
• Partner with security, compliance, and client teams to ensure AI platforms align with enterprise security, privacy, regulatory, and data-residency requirements.
• Implement logging, monitoring, auditing, vulnerability management, disaster-recovery, and business-continuity practices for production AI services.

Data Platform & RAG Enablement

• Build and support secure data-ingestion, transformation, indexing, and retrieval pipelines for enterprise AI applications.
• Design platform patterns for RAG, including document ingestion, parsing, chunking, metadata enrichment, embeddings, vector stores, hybrid search, retrieval, reranking, and source attribution.
• Integrate AI applications with structured and unstructured enterprise data sources, including databases, data warehouses, document repositories, knowledge bases, ServiceNow, and third-party SaaS platforms.
• Work with data engineers to establish data-quality, lineage, cataloging, permissions, retention, and governance practices that support trustworthy AI.
• Enable appropriate data-access controls so AI solutions retrieve and process only data the requesting user or service is authorized to access.
• Support data platforms and technologies such as Snowflake, Databricks, PostgreSQL, MongoDB, Elasticsearch/OpenSearch, vector databases, and cloud storage services, as appropriate.

Responsible AI, Governance & Observability

• Implement technical controls that support r