AI Platform Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Design, develop, implement, and sustain scalable platform capabilities for enterprise AI, machine learning, LLM, RAG, and agent-based AI applications.

• Create reusable reference architectures, infrastructure patterns, deployment templates, integration components, and engineering standards.

• Establish secure integrations with the Anthropic API, Claude models, enterprise data, APIs, workflow systems, and authorized tools.

• Set up CI/CD, LLMOps, MLOps, versioning, testing, release management, rollback, monitoring, and lifecycle-management practices.

• Provide support for production AI services through incident response, troubleshooting, root-cause analysis, capacity planning, and service-level monitoring.

• Design and oversee cloud infrastructure across AWS, Microsoft Azure, Google Cloud Platform, or client-approved environments.

• Implement containerized deployments, infrastructure as code, identity and access controls, security, privacy, compliance, 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, human-in-the-loop workflows, auditability, and governance controls.

• Create integrations between AI platforms, ServiceNow, enterprise APIs, identity providers, workflow tools, and line-of-business systems.

• Work collaboratively with AI Architects, Forward Deployed AI Engineers, data engineers, ServiceNow teams, product engineering, and client stakeholders.

• Provide technical guidance, contribute to playbooks and documentation, and engage in architecture reviews, demos, technical discovery, and customer workshops.

• Deliver reusable platform components, reference architectures, and playbooks for the NewRocket Intelligence Platform, Data Intelligence Platform, and Agent Pack ecosystem.

⛳️ Requirements

• Over 5 years of experience in platform engineering, cloud engineering, DevOps, software engineering, data engineering, systems integration, or related technical roles.

• Practical experience in designing and deploying cloud-native applications and services on AWS, Microsoft Azure, and/or Google Cloud Platform.

• Extensive experience with CI/CD, Git-based workflows, automated testing, infrastructure as code, and production release processes.

• Familiarity with Docker, Kubernetes, serverless services, or similar cloud-native platforms.

• Proficient in Python, JavaScript/TypeScript, Java, Go, Bash, or comparable programming and scripting languages.

• Experience in designing and consuming REST APIs, along with implementing authentication and authorization patterns.

• Hands-on experience with LLM-powered applications, generative AI services, AI/ML platforms, RAG systems, or AI workflow automation.

• Knowledge of 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 understanding 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.

• Excellent problem-solving, troubleshooting, communication, and documentation skills.

• Ability to thrive in a fast-paced, collaborative, customer-focused environment.

• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related technical discipline; equivalent relevant professional experience will be accepted.

• Relevant certifications are an advantage.

🏝️ Benefits

• Diverse and inclusive workplace.

• Equal opportunity and affirmative action employer.

• Disability accommodations available.