Note: The job is a remote job and is open to candidates in USA. NewRocket is an AI-first Elite ServiceNow Partner that helps enterprises adopt trusted AI and realize value on the Now Platform. The AI Platform Engineer will build, operate, and improve secure, scalable foundations for enterprise AI solutions, including Claude-powered applications, RAG, agentic workflows, cloud infrastructure, LLMOps, data integrations, observability, and governance. The role also partners with architects, engineers, ServiceNow teams, and customers to create reusable platforms and production-ready AI capabilities.
Responsibilities
- 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
- 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
- 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
- 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
- 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
- Implement technical controls that support responsible, secure, and governable AI deployments
- Build safeguards for sensitive-data handling, data masking, content filtering, prompt injection, unsafe tool use, unauthorized access, and unintended agent behavior
- Enable grounding, output validation, source attribution, confidence thresholds, fallback behavior, approval gates, and human-in-the-loop workflows
- Implement AI observability and tracing across prompts, model calls, retrieval pipelines, tool execution, workflow outcomes, latency, errors, costs, and user feedback
- Partner with AI Architects and governance stakeholders to document platform standards, risk controls, operating procedures, and solution limitations
- Support auditability and compliance requirements through appropriate logging, retention, access reviews, and operational documentation
- Build and maintain integration patterns between AI platforms, ServiceNow, enterprise APIs, identity providers, workflow tools, collaboration platforms, and line-of-business systems
- Support technical enablement for ServiceNow AI and workflow experiences, including IntegrationHub, Flow Designer, Virtual Agent, Now Assist, AI Agents, APIs, and knowledge-management capabilities where applicable
- Develop secure APIs, middleware services, event-driven integrations, and automation components that support AI-enabled workflows
- Collaborate with Forward Deployed AI Engineers to troubleshoot complex client integrations and transition successful engagement solutions into reusable platform components
- Work closely with AI Architects, AI/ML Engineers, Data Engineers, Product Engineering, ServiceNow developers, Business Process Consultants, and client technology teams
- Provide technical guidance on AI platform engineering, cloud architecture, DevOps, LLMOps, data integration, performance, and security best practices
- Contribute to internal playbooks, runbooks, reference architectures, technical documentation, reusable modules, and knowledge-sharing sessions
- Identify recurring client requirements and convert them into scalable, productized platform features and accelerators
- Participate in technical discovery, architecture reviews, demos, implementation planning, and customer workshops as needed
Skills
- **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 containerization and orchestration technologies such as **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 LLM application concepts, including prompt and context engineering, token management, embeddings, vector search, RAG, structured outputs, tool use/function calling, evaluations, and model monitoring
- Experience with observability tools and practices, including 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 working with data systems such as 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
- Hands-on experience with **Claude**, the **Anthropic API**, Anthropic Console, Claude Code, or Anthropic technical guidance
- Completion of Anthropic Academy learning, partner enablement, technical training, or equivalent Claude implementation experience
- Experience with **Model Context Protocol (MCP)**, secure tool integrations, agent gateways, or comparable methods for connecting AI applications to enterprise systems
- Experience with LLM application frameworks and orchestration tools such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, OpenAI Agents SDK, or comparable technologies
- Experience implementing LLM evaluation, prompt/version management, AI tracing, guardrails, and AI observability platforms
- Experience operating model gateways, API gateways, or AI-service routing layers
- Experience with MLOps platforms and tools such as MLflow, SageMaker, Vertex AI, Azure Machine Learning, Databricks, Kubeflow, or comparable services
- Experience with data engineering, ETL/ELT, streaming, data orchestration, data-quality testing, data governance, and data-catalog capabilities
- Experience with vector databases and enterprise search technologies such as Pinecone, Weaviate, pgvector, OpenSearch, Elasticsearch, Azure AI Search, or similar platforms
- Experience with Terraform, Pulumi, CloudFormation, Bicep, Helm, Argo CD, GitHub Actions, GitLab CI/CD, Azure DevOps, Jenkins, or comparable tooling
- Experience with Kubernetes operations, service meshes, API management, event-driven architecture, and microservices
- Familiarity with FinOps practices and optimization of cloud, model, inference, storage, and data-processing costs
- Experience with ServiceNow architecture, development, integrations, platform operations, or workflow automation
- Familiarity with ServiceNow APIs, IntegrationHub, Flow Designer, Virtual Agent, Now Assist, AI Agents, CMDB, knowledge management, and enterprise data-integration patterns
- Experience working in consulting, professional services, enterprise architecture, or client-facing technical delivery environments
- Relevant certifications in cloud platforms, Kubernetes, DevOps, security, data engineering, ServiceNow, AI/ML, or Anthropic technologies are a plus
Company Overview