Sr. Platform Engineer

Our Enterprise Software team powers the digital backbone behind Rivian’s Enterprise operations. We build the systems that keep our factories running, parts flowing, and vehicles delivered on time from planning and production to logistics and supplier ecosystems. We’re looking for a Senior Platform Engineer who is excited to build at the intersection of enterprise systems and AI. This role will be located in Atlanta, GA and report to our Sr. Manager, Software Engineering.

• Infrastructure as Code: Implement and maintain IaC principles using Terraform and CloudFormation to provision and manage cloud resources on AWS.
• Cloud Architecture: Design and implement scalable, high-availability cloud architectures on AWS, optimizing for performance and cost-efficiency.
• Kubernetes Orchestration: Deploy, manage, and monitor containerized applications using Kubernetes, ensuring seamless scaling and resilience.
• Automation & Scripting: Develop and maintain scripts and automation tools to streamline deployment, configuration management, and system maintenance.
• Platform Development: Contribute to the evolution of our platform, collaborating with development teams to ensure smooth integration and deployment.
• Monitoring & Observability: Implement and manage monitoring and logging solutions to proactively identify and address issues.
• Troubleshooting & Incident Response: Participate in incident response, troubleshooting, and resolving issues to minimize downtime and impact.
• AI Platform Operations: Support deployment and operation of AI/ML services (model serving, inference APIs, embedding pipelines) with the same reliability, scaling, and rollback rigor applied to traditional applications.
• AI Safety in Delivery: Partner with development and security teams to integrate evaluation, policy, and rollout controls for AI features into deployment pipelines.

• Strong Experience: Proven experience with Infrastructure as Code, Terraform, CloudFormation and AWS cloud infrastructure management.
• Kubernetes Proficiency: Hands-on experience deploying, managing, and troubleshooting Kubernetes clusters.
• Scripting & Coding: Fluency in scripting/coding languages (e.g., Python, Bash, PowerShell) for automation and tooling.
• Terraform Module Development: Ability to create reusable Terraform modules to streamline infrastructure provisioning
• Platform Mindset: A collaborative approach to working with development teams to build and maintain a robust platform.
• Cloud Security: Understanding of cloud security best practices and the ability to implement secure configurations.
• Problem-Solving: Excellent troubleshooting and problem-solving skills to address complex infrastructure issues.
• AI Workload Familiarity: Exposure to operating AI/ML infrastructure - model serving, GPU or accelerated compute, vector/RAG backends, or managed AI services on AWS (e.g., Bedrock, SageMaker).

Nice to Have:

• CI/CD Pipelines: Experience building and maintaining Continuous Integration and Continuous Deployment (CI/CD) pipelines.
• MLOps / LLMOps Tooling: Experience with model lifecycle, prompt/version management, or evaluation pipelines (e.g., MLflow, Kubeflow, W&B, or equivalent).
• AI Developer Tooling: Experience enabling org-scale AI-assisted development workflows with SSO, policy, and audit controls.
• Vector & RAG Infrastructure: Hands-on experience with embedding pipelines, vector databases, or retrieval services in production.
• Kubernetes Certifications: Relevant certifications (e.g., Certified Kubernetes Administrator, Certified Kubernetes Application Developer)
• AWS Certifications: Relevant certifications (e.g., AWS Solutions Architect, DevOps Engineer) demonstrate your expertise.