Platform Engineer I

Overview:

What you will do:

• Design, develop, and maintain automation tools and platform capabilities using Go, Python, or similar languages to help eliminate manual toil and build self-service components across infrastructure, observability, reliability, and security domains.
• Build and enhance internal tools, integrations, and workflows that improve operational efficiency and developer productivity within the SaaS platform.
• Develop and support automation frameworks for incident response, infrastructure testing, monitoring optimization, and compliance validation, including CI/CD pipeline enhancements.
• Help develop observability and monitoring solutions including setting up alerts, contributing to noise reduction systems, automated SLO management, and cost optimization.
• Partner closely with SaaS Engineering to deploy application and infrastructure updates using automation tools.
• Available for on-call duties for incident response, blameless postmortems, and helping translate learnings into automated solutions.
• Contribute to build playbook documentation and procedures.
• What we are looking for:
• Bachelor’s degree in Computer Science, Engineering, or a related field (equivalent work experience will be considered in lieu of a degree).
• 3-5 years of experience in software development, platform engineering, Site Reliability Engineering (SRE), or DevOps, with hands-on experience supporting production environments.
• Strong technical expertise in Kubernetes, cloud platforms (AWS, Azure, or GCP), Go/Python development, Linux, Infrastructure as Code (Terraform, CloudFormation, Ansible), CI/CD, container technologies, APIs, microservices, distributed systems, and SaaS production operations.
• AI Skills/Knowledge:
• Familiarity with AI-assisted tools such as Microsoft Copilot, GitHub Copilot, Claude, Cursor, or similar tools to support troubleshooting, root cause analysis (RCA), coding, code review, and operational problem solving.
• Ability to leverage AI tools to improve infrastructure automation, scripting, documentation, test generation, and engineering productivity.
• Familiarity with LLM APIs and agentic AI workflows, with an interest in applying AI capabilities to platform engineering, DevOps, and reliability engineering workflows.
• Awareness of responsible AI principles, including security, bias, explainability, and appropriate use of AI-generated outputs.

Preferred Skills:

• Experience contributing to automation projects, internal tools, or monitoring implementations (i.e. Datadog, Prometheus, Grafana, Backstage) is a plus.
• Experience with Kubernetes operators, controllers, or orchestration concepts is a plus.
• FedRAMP, SOC2, or compliance framework exposure is a plus.
• #LI-SR1
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