AI Infrastructure Engineer; Senior Associate

• AI Infrastructure Development:
  Build, integrate, and operate production infrastructure supporting governed AI usage, working with approved architectures and model-access patterns, including Amazon Bedrock where applicable.
• Infrastructure as Code and Automation:
  Develop infrastructure-as-code solutions, deployment automation, reusable platform components, and continuous integration and delivery (CI/CD) templates to improve consistency, reliability, and scalability.
• AI Usage Monitoring and Cost Management:
  Implement telemetry and data pipelines for token consumption, model usage, user and project attribution, quotas, cost tracking, and financial reporting to support transparency and effective resource management.
• Agent Execution Environments:
  Deploy and maintain approved sandbox environments for coding agents and other tool-using AI agents, ensuring secure execution and appropriate operational controls.
• Secure Service Integration:
  Build governed mediation and integration patterns for AI applications and agents, including Model Context Protocol (MCP) or equivalent approaches, following established architectural and security requirements.
• Observability and Operational Readiness:
  Implement logging, monitoring, alerting, audit evidence collection, and operational reporting for onboarded AI workloads. Help ensure systems are supportable, traceable, and aligned with enterprise requirements.
• Reusable Platform Solutions:
  Create onboarding templates, reference implementations, and reusable infrastructure components that enable teams across AI delivery, enterprise IT, analytics, engineering, consulting, and internal operations to adopt approved AI capabilities efficiently.
• Cross-Functional Collaboration:
  
  Partner with AI platform leadership and multidisciplinary teams to deliver the infrastructure and platform components that support internal business solutions and client-facing initiatives.
• AI-Assisted Engineering:
  Use AI tools to accelerate infrastructure coding, automation, debugging, test development, documentation, and runbook creation while maintaining appropriate quality and security standards.
• Documentation and Support:
  Develop and maintain technical documentation, operational runbooks, support models, incident escalation procedures, and operational handoffs to ensure reliable ongoing service delivery.

• Professional
  
  Experience:
  
  At least 3 years of experience building and operating production cloud infrastructure or platform engineering solutions, with hands-on exposure to AWS, automation, infrastructure as code, or production operations.
• Infrastructure as Code:
  Solid experience defining, provisioning, and managing infrastructure through code and repeatable deployment processes.
• Cloud and Security Fundamentals:
  Practical knowledge of cloud networking, identity and access management (IAM), secrets management, logging, monitoring, deployment automation, and operational support.
• Programming and Scripting:
  Software engineering or scripting skills sufficient to develop automation, infrastructure tooling, integration components, and operational utilities.
• Practical AI Tool Usage:
  Demonstrated ability to use AI tools to accelerate infrastructure development, code generation, debugging, testing, automation, and technical documentation.
• APIs and Systems Integration:
  Familiarity with APIs, event-driven architectures, service integration, and secure deployment patterns.
• Engineering Execution:
  Ability to translate established architectural patterns and technical requirements into working systems, resolve implementation challenges, and deliver reliable solutions with guidance from senior engineers and architects.
• Problem-Solving and Adaptability:
  Strong analytical thinking, attention to detail, and a willingness to learn emerging technologies in a rapidly evolving AI infrastructure environment.
• Collaboration and Communication:
  Ability to work effectively with architects, engineers, operations teams, and business stakeholders while communicating technical decisions and implementation progress clearly.
• Operational Mindset:
  Understanding of production reliability, troubleshooting, documentation, incident handling, and the importance of secure, maintainable infrastructure.
• Location and Travel:
  Ability to work from a location within the United States and travel as needed.

Preferred qualifications:

• Experience with Amazon Bedrock or other managed AI and model-hosting platforms.
• Familiarity with token and cost telemetry, usage attribution,…