AI Infrastructure Engineer (Senior Associate)

Accountabilities: 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. Requirements 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, chargeback, showback, or FinOps data pipelines. Experience with durable agentic AI orchestration platforms used for infrastructure automation, agent workflows, or operational processes. Knowledge of containerized or sandboxed execution environments. Experience with AI agent tools, MCP, model gateways, API gateways, or secure service-broker architectures. Experience supporting regulated or sensitive workloads involving confidential client information, protected health information (PHI), or personally identifiable information (PII). Benefits Competitive base salary: Estimated annual base salary ranging from $120,000 to $165,000 , depending on experience, qualifications, certifications, and other relevant factors. Performance-based compensation: Eligibility for an annual incentive compensation program, with estimated total compensation, including incentive opportunities, ranging from $138,000 to $189,000 . Healthcare coverage: Access to medical, dental, and vision insurance plans, subject to applicable eligibility requirements. Wellness programs: Access to employee wellness initiatives and support programs. Retirement and financial benefits: Eligibility for company benefit plans and applicable retirement savings programs. Remote work flexibility: Flexible living locations across the United States, with travel as needed. Professional development: Opportunities to deepen expertise in cloud infrastructure, AI platforms, automation, and enterprise-grade technology solutions. Technical ownership: The opportunity to build reusable infrastructure and contribute to the architecture and operational foundations of enterprise AI systems. Collaborative environment: Work alongside experienced architects, engineers, and multidisciplinary teams on innovative technology initiatives. Career growth: Develop valuable expertise at the intersection of cloud engineering, AI infrastructure, security, and platform operations.