AI Platform Architect
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Job Description
AI Platform Engineer building secure, scalable Claude and RAG platforms. Enabling governed AI integrations across NewRocket’s ServiceNow enterprise ecosystem.
Responsibilities:
- 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:
- 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:
- Diverse and inclusive workplace
- Equal opportunity and affirmative action employer
- Disability accommodation support
- Travel based on client and business needs
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