AI Platform Architect

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 agentic 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.

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

• Execute infrastructure as code, containerized deployments, identity and access controls, security, privacy, compliance, logging, monitoring, auditing, disaster recovery, and business continuity.

• Develop 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.

• Create 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.

• Transform recurring client requirements into scalable productized platform features and accelerators.

⛳️ Requirements

• A minimum of 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.

• Strong background in CI/CD, Git-based workflows, automated testing, infrastructure as code, and production release processes.

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

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

• Experience in designing and utilizing REST APIs, integrating enterprise applications, and implementing authentication and authorization methods.

• 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 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.

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

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

• Relevant certifications are a plus, but not mandatory.

🏝️ Benefits

• A diverse and inclusive workplace.

• Equal opportunity and affirmative action employer.

• Support for disability accommodations.

• Travel may be required based on client and business needs.