Platform DevOps Engineer

PVcase is a company dedicated to advancing solar energy solutions through innovative software applications. They are seeking a Platform DevOps Engineer to contribute to the development of global infrastructure standards and support the PVcase Prospect SaaS application, focusing on scalability, reliability, and automation.

Responsibilities:

• Direct the AWS infrastructure strategy for PVcase Prospect, ensuring the application meets rigorous availability, performance, and security benchmarks
• Collaborate with the Global Platform team to implement unified architectural standards, contributing to organization-wide IaC and security initiatives
• Architect and maintain resilient cloud environments using Terraform and AWS, prioritizing modularity and reuse
• Support the transition toward a self-service enablement model, providing product developers with the tools and guardrails necessary for autonomous deployments
• Manage and refine monitoring, logging, and alerting stacks (Grafana, ELK, Prometheus, Checkly) to ensure proactive incident detection
• Identify and implement opportunities to leverage agentic workflows and AI-assisted tooling to automate complex operational tasks and improve incident response times

Requirements:

• Extensive hands-on experience managing complex AWS ecosystems (including VPC, RDS, IAM, EKS, Route53, S3, EFS, Firebase)
• Proven proficiency with Terraform for infrastructure automation and Kubernetes/Docker for container orchestration
• Strong command of cloud networking (subnets, load balancing, routing) and DevSecOps principles (RBAC, encryption, secret management)
• A pragmatic approach to engineering, with a track record of delivering incremental improvements in high-growth SaaS environments
• Excellent communication skills, with the ability to act as a technical liaison between US-based product teams and our global infrastructure team
• Professional familiarity with, or a strong aptitude for, implementing AI-driven agentic workflows to optimize DevOps processes. This includes the use of LLMs and autonomous agents for task automation, documentation, and infrastructure maintenance
• Comfort utilizing AI-assisted development tools (e.g., GitHub Copilot, Claude Code) to accelerate code generation and infrastructure troubleshooting
• A proactive interest in evaluating emerging technologies that reduce cognitive load and enhance developer velocity