Platform Engineer

Role Overview

• Own availability, latency, and performance targets for AI platform services and data infrastructure running on AWS
• Design and implement monitoring, alerting, and observability frameworks across the platform stack
• Lead incident response, root cause analysis, and post-mortem processes for platform-level outages or degradations
• Define and track SLOs/SLAs for core platform primitives including RAG pipelines, agent orchestration services, and model access layers
• Proactively identify reliability risks and drive engineering improvements before they become production issues
• Build and maintain runbooks, disaster recovery procedures, and operational documentation
• Design, build, and maintain CI/CD pipelines for AI platform components, data pipelines, and internal applications
• Own infrastructure-as-code (IaC) practices across the team using tools such as Terraform or AWS CDK
• Manage and optimize AWS environments including ECS, Lambda, S3, RDS, Redshift, API Gateway, and related services
• Implement and enforce security, compliance, and cost optimization best practices across AWS infrastructure
• Automate deployment, scaling, and configuration management to reduce manual operational overhead
• Partner with AI Platform Engineers to containerize and operationalize AI services and agent frameworks
• Support Data & AI Engineers with environment management, access controls, and deployment tooling for Polaris and data pipeline infrastructure
• Serve as the operational backbone for the AI platform team – ensuring engineers can ship and iterate quickly without being blocked by infrastructure concerns
• Contribute to our “factory model” vision by making deployment and reliability a repeatable, scalable capability rather than an ad hoc function

Requirements

• 3+ years of professional experience in a DevOps, SRE, or platform engineering role
• Hands-on AWS experience required – AgentCore, Bedrock, ECS, Lambda, S3, RDS, Redshift, CloudWatch, IAM, VPC, and related services
• Experience with infrastructure-as-code tools such as Terraform or AWS CDK
• Strong CI/CD experience with tools such as GitHub Actions
• Experience with containerization and orchestration (Docker, ECS, or Kubernetes)
• Familiarity with AI/ML infrastructure patterns – model serving, vector databases, pipeline orchestration (strongly preferred)
• Experience with observability and monitoring tooling (Datadog, CloudWatch)
• Prior experience in a SaaS environment
• Strong verbal and written communication skills with ability to collaborate across technical and non-technical stakeholders
• Self-starter with a proactive approach to identifying and resolving infrastructure risk before it impacts delivery
• Willingness to explore and adopt AI tools responsibly to enhance productivity and innovation in your role.

Tech Stack

• Amazon Redshift
• AWS
• Docker
• Kubernetes
• Terraform

Benefits

• competitive health plans
• paid time-off
• company paid holidays
• 401K retirement program with a Company elected match
• other company sponsored programs