Senior Platform Engineer

📋 Job Overview

Raccoon AI is building a generative AI customer service SaaS platform, serving enterprise customers across e-commerce, travel, and fintech industries. The system consists of multiple microservices (AI inference backend, retrieval service, tool service, and multi-tenant management platform), all running on AWS. The current deployment and infrastructure are maintained by the engineering team, operating mainly through scripts and manual configurations. We are looking for a Senior Platform Engineer to take over and systematize the entire cloud platform: infrastructure IaC, enhancing reliability and observability, converging multi-environment complexity, and building a platform that allows engineers to safely, quickly, and self-serve publish versions. This is a highly autonomous, cross-stack role with complete technical decision-making authority.

🏢 About Raccoon AI

Raccoon AI creates a generative AI customer service SaaS platform, serving enterprise customers across e-commerce, travel, and fintech industries.

🎯 The Role

You will be responsible for maintaining and enhancing the CI/CD pipeline, infrastructure IaC, container and deployment management, observability and reliability, data layer operations, security and key management, cost optimization, and converging environment complexity.

✅ Key Responsibilities

• CI/CD Ownership: Maintain and enhance GitHub Actions (multi-environment branch deployment, tag-controlled production, canary, Docker Buildx cache).
• Infrastructure IaC: Convert manually/scripted AWS resources (ECS, task definition, IAM, network) into version-controlled, reproducible, auditable Terraform.
• Container and Deployment: Manage ECS Fargate (ARM64/Graviton), Lambda, and Rails services deployed with Kamal; implement zero-downtime, blue-green, canary deployments and one-click rollback.
• Observability and Reliability: Integrate logs/metrics/tracing/alerts, establish SLOs, improve incident response and on-call processes.
• Data Layer Operations: PostgreSQL (including PGVector) operations, backup, connection pool tuning, and migration support.
• Security and Key Management: IAM minimum permissions, SSM/Secrets Manager, supply chain and image security.
• Cost Optimization: Right-sizing, ARM/Graviton migration, Lambda cold start, caching strategies, controlling cloud and inference costs.
• Converging Environment Complexity: Tidy up staging/beta/production and multiple developer environments, while supporting Ruby/Rails and Python/FastAPI.

📌 Required Qualifications

• 5+ years of DevOps/SRE/Platform Engineering experience, with formal AWS production operations experience.
• Solid AWS skills: ECS/Fargate, Lambda, ECR, IAM, VPC, CloudWatch, SSM, S3.
• Production experience with Docker and containerized deployment.
• Design and ownership of CI/CD pipelines (GitHub Actions preferred).
• Experience with Terraform (IaC) implementation.
• Linux + solid Bash/Python automation capabilities.
• Zero-downtime deployment, monitoring/alerting, incident response/on-call experience.
• Key management and cloud security practices.

⭐ Desirable Experience

• ARM/Graviton deployment and cost optimization experience.
• Kamal or Rails application deployment experience.
• Ability to support both Ruby and Python services.
• PostgreSQL/PGVector or vector database operations.
• LLM/AI service infrastructure experience (inference cost, latency, Langfuse-like tracing).
• Observability stack, Cloudflare, Slack ChatOps.

🎁 Benefits

Competitive salary and benefits package.