AI Platform Architect

NewRocket

AI Platform Engineer building secure, scalable Claude and RAG platforms for NewRocket, an AI-first ServiceNow partner. Enabling governed enterprise integrations, LLMOps, and production AI delivery.

Posted 8/27/2026full-timeRemote • 🇳🇱 NetherlandsMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying cloud-native applications and services, with a strong focus on AI and machine learning integration. Proficient in CI/CD practices, infrastructure as code, and secure data management across various cloud platforms.

Highest-signal resume keywords

Cloud EngineeringCI/CD ImplementationAI/ML IntegrationInfrastructure As CodePlatform Engineering

ATS Keywords

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Hard Skills

PythonJavaScriptJavaGoBashDockerKubernetesREST APIsLLM ApplicationsData Engineering

Soft Skills

Problem-SolvingTroubleshootingCommunicationDocumentationCollaboration

Tools & Technologies

AWSMicrosoft AzureGoogle Cloud PlatformServiceNowTerraformAnthropic APIClaude ModelsVector DatabasesNoSQL DatabasesData Warehouses

Industry Keywords

Platform EngineeringDevOpsMLOpsAI Workflow AutomationCloud SecurityIdentity ManagementGenerative AIIncident ManagementObservabilityData Intelligence

Tech Stack

Tools & technologies

AWSAzureCloudDockerGoGoogle Cloud PlatformJavaJavaScriptKubernetesNoSQLPythonServiceNowTerraformTypeScript

About the role

Key responsibilities & impact

Design, build, deploy, and maintain scalable platform capabilities supporting enterprise AI, machine learning, LLM, RAG, and agentic AI applications
Create reusable reference architectures, infrastructure patterns, deployment templates, integration components, and engineering standards
Build secure integrations with enterprise data, APIs, workflow systems, authorized tools, Anthropic API, and Claude models
Enable standardized authentication, model access, prompt and context management, structured outputs, tool use, logging, error handling, and rate-limit management
Support Claude-based document analysis, knowledge assistance, workflow automation, agentic task execution, summarization, classification, and decision support
Establish and operate CI/CD pipelines, LLMOps and MLOps capabilities, evaluation pipelines, deployment automation, monitoring, and lifecycle management
Support production AI services through incident response, troubleshooting, root-cause analysis, capacity planning, and service-level monitoring
Define and monitor availability, latency, throughput, token consumption, model cost, tool-call success rates, task-completion rates, and error rates
Design and manage cloud infrastructure across AWS, Microsoft Azure, Google Cloud Platform, or client-approved environments
Implement infrastructure as code, containerized deployments, identity and access controls, security, privacy, auditing, vulnerability management, disaster recovery, and business continuity
Build secure data-ingestion, transformation, indexing, retrieval, RAG, embeddings, vector-store, hybrid-search, and source-attribution pipelines
Integrate AI applications with databases, data warehouses, document repositories, knowledge bases, ServiceNow, and third-party SaaS platforms
Implement responsible-AI safeguards, observability, tracing, output validation, approval gates, fallback behavior, and human-in-the-loop workflows
Build integrations between AI platforms, ServiceNow, enterprise APIs, identity providers, workflow tools, collaboration platforms, and line-of-business systems
Provide technical guidance and collaborate with AI, data, product, ServiceNow, consulting, and client technology teams
Contribute to playbooks, runbooks, reference architectures, technical documentation, reusable modules, knowledge-sharing sessions, architecture reviews, demos, implementation planning, and customer workshops
Productionize AI solutions and contribute reusable components to NewRocket’s Intelligence Platform, Data Intelligence Platform, and Agent Pack ecosystem

Requirements

What you’ll need

5+ years of experience in platform engineering, cloud engineering, DevOps, software engineering, data engineering, systems integration, or related technical roles
Hands-on experience designing and deploying cloud-native applications and services on AWS, Microsoft Azure, and/or Google Cloud Platform
Strong experience with CI/CD, Git-based workflows, automated testing, infrastructure as code, and production release processes
Experience with Docker, Kubernetes, serverless services, or comparable cloud-native platforms
Proficiency in Python, JavaScript/TypeScript, Java, Go, Bash, or similar programming and scripting languages
Experience designing and consuming REST APIs, integrating enterprise applications, and implementing authentication and authorization patterns
Hands-on experience with LLM-powered applications, generative AI services, AI/ML platforms, RAG systems, AI workflow automation, or related technologies
Familiarity with prompt and context engineering, token management, embeddings, vector search, RAG, structured outputs, tool use/function calling, evaluations, and model monitoring
Experience with logging, metrics, tracing, alerting, and incident management
Strong knowledge of cloud security, identity and access management, secrets management, network security, and secure software-development practices
Experience with relational databases, NoSQL databases, data warehouses, object storage, search platforms, or vector databases
Strong problem-solving, troubleshooting, communication, and documentation skills
Ability to work effectively in a fast-paced, collaborative, customer-oriented environment
Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related technical discipline; equivalent relevant professional experience will be considered
Preferred: hands-on Claude, Anthropic API, Anthropic Console, Claude Code, or Anthropic technical guidance experience
Preferred: Anthropic Academy learning, partner enablement, technical training, or equivalent Claude implementation experience
Preferred: Model Context Protocol, LLM frameworks, evaluation, guardrails, observability, MLOps, data engineering, vector databases, Terraform, Kubernetes operations, ServiceNow, consulting, professional services, enterprise architecture, or client-facing technical delivery experience
Relevant certifications are a plus

Benefits

Comp & perks

Diverse and inclusive workplace
Equal opportunity workplace and affirmative action employer
Reasonable accommodation available for individuals with disabilities