AI Platform Architect

NewRocket

AI Platform Engineer building secure, scalable Claude and RAG platforms. Enabling governed AI integrations across NewRocket’s ServiceNow enterprise ecosystem.

Posted 8/27/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying scalable AI and machine learning platforms, with a strong focus on cloud infrastructure management and CI/CD practices. Proficient in implementing secure integrations and responsible AI safeguards while collaborating effectively with cross-functional teams.

Highest-signal resume keywords

Platform EngineeringCloud Infrastructure ManagementCI/CD Pipeline ImplementationAI/ML Application DevelopmentInfrastructure as Code

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonJavaScript/TypeScriptJavaGoBashDockerKubernetesREST API DesignCloud SecurityData Engineering

Soft Skills

CollaborationProblem-SolvingCustomer Orientation

Tools & Technologies

AWSMicrosoft AzureGoogle Cloud PlatformServiceNowLLMOpsMLOps

Industry Keywords

Generative AIRAG SystemsIncident ManagementIdentity and Access ManagementData Integration

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 and operate CI/CD pipelines, LLMOps and MLOps capabilities, versioning, testing, release management, rollback, and change control
Support production AI operations, including 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, compliance, logging, monitoring, auditing, 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, evaluation, output validation, human-in-the-loop workflows, and governance controls
Build integrations between AI platforms, ServiceNow, enterprise APIs, identity providers, workflow tools, collaboration platforms, and business systems
Collaborate with AI Architects, engineers, ServiceNow teams, product engineering, security, data teams, and client stakeholders
Contribute to technical documentation, playbooks, runbooks, reference architectures, reusable modules, demos, architecture reviews, and customer workshops
Convert recurring client requirements into scalable productized platform features and accelerators

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
Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related technical discipline; equivalent relevant professional experience will be considered
Ability to work effectively in a fast-paced, collaborative, customer-oriented environment
Relevant certifications are a plus, not required

Benefits

Comp & perks

Diverse and inclusive workplace
Equal opportunity and affirmative action employer
Disability accommodation support
Travel based on client and business needs