AI Platform Architect

NewRocket

AI Platform Engineer building secure, scalable Claude-powered enterprise AI platforms. Enabling RAG, agentic workflows, ServiceNow integrations, and governed production deployments for enterprise clients.

Posted 9/10/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and deploying scalable AI and machine learning platforms, with a strong focus on cloud infrastructure management, CI/CD practices, and secure integrations. Proficient in programming languages and tools essential for building enterprise-level applications and services.

Highest-signal resume keywords

Cloud Infrastructure ManagementCI/CD ImplementationAI/ML Platform DevelopmentInfrastructure As CodeSecure API Integration

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

PythonJavaScriptJavaGoBashDockerKubernetesREST API DesignLLM Application DevelopmentData Engineering

Soft Skills

CollaborationCustomer OrientationProblem Solving

Tools & Technologies

AWSMicrosoft AzureGoogle Cloud PlatformServiceNowAnthropic APIMLOpsVector DatabasesCI/CD ToolsLogging and Monitoring ToolsIdentity and Access Management

Certifications & Qualifications

Bachelor’s Degree in Computer ScienceEngineeringInformation SystemsData Science

Industry Keywords

Platform EngineeringCloud EngineeringDevOpsGenerative AIAI Workflow Automation

Tech Stack

Tools & technologies

AWSAzureCloudDockerGoGoogle Cloud PlatformJavaJavaScriptKubernetesNoSQLPythonServiceNowTypeScript

About the role

Key responsibilities & impact

Design, build, deploy, and maintain scalable platform capabilities for 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 the Anthropic API, Claude models, enterprise data, APIs, workflow systems, and authorized tools
Establish CI/CD, LLMOps, MLOps, versioning, testing, release-management, rollback, monitoring, evaluation, and lifecycle-management practices
Support production AI services through incident response, troubleshooting, root-cause analysis, capacity planning, and service-level monitoring
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, logging, auditing, vulnerability management, disaster recovery, and business continuity
Build secure data-ingestion, transformation, indexing, retrieval, RAG, vector-search, and enterprise data-integration pipelines
Implement responsible AI safeguards, observability, tracing, output validation, source attribution, approval gates, 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 Architects, AI/ML Engineers, Data Engineers, Product Engineering, ServiceNow developers, consultants, and client technology teams
Contribute to playbooks, runbooks, reference architectures, technical documentation, reusable modules, demos, architecture reviews, and customer workshops
Productionize Claude-powered and other enterprise AI solutions for secure, scalable, observable, governed use

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
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 qualifications include hands-on Claude, Anthropic API, MCP, LLM frameworks, MLOps, vector databases, Kubernetes operations, ServiceNow, consulting, professional services, enterprise architecture, or client-facing technical delivery experience

Benefits

Comp & perks

Diverse and inclusive workplace
Equal opportunity workplace and affirmative action employer
Disability accommodations available upon request
Relevant certifications in cloud platforms, Kubernetes, DevOps, security, data engineering, ServiceNow, AI/ML, or Anthropic technologies are a plus