Sr. Production Engineer, Solutions Engineering

What you’ll do

• Design and build AI agents that augment production reliability work — Develop agents that assist engineers with service health analysis, reliability recommendations, migration playbook generation, and risk identification
• Drive large-scale infrastructure modernization with AI-accelerated execution — Lead Kubernetes adoption and platform transitions using AI to generate automation
• Transform consulting patterns into scalable platforms — Execute scoped reliability engagements with engineering teams, then encode successful approaches into AI-assisted tools and automation
• Build the knowledge infrastructure that powers Pinterest's operational agent ecosystem — Create migration playbooks, operational runbooks, incident patterns, and best practices
• Develop software solutions to enable reliability and operability of large-scale distributed systems
• Build tools and automation to eliminate toil and reduce operational overhead
• Build meaningful, insightful and actionable SLIs — Develop service level indicators that provide clear signals of system health
• Automate critical portions of Pinterest's engineering processes
• Manage capacity and performance to help scale our infrastructure — Partner with teams to plan and optimize capacity across public and private clouds

What we’re looking for

• 5+ years of industry experience building and operating large-scale, high-performance distributed systems
• Bachelor's degree in Computer Science or related field, or equivalent experience
• Strong programming skills in Python or Go — ability to build production-grade platforms, agents, and automation
• Deep knowledge of Linux/Unix internals and experience with open source infrastructure (MySQL, Kafka, Envoy, Hadoop, etc.)
• Infrastructure as Code experience (Terraform, Puppet, Chef, Ansible, Docker, Kubernetes)
• Experience deploying web applications to cloud infrastructure (AWS, GCP, or Azure) and working with distributed, service-oriented architecture

Preferred

• Experience developing AI agents for infrastructure automation, operational decision-making, or reliability workflows
• AI/ML infrastructure experience (LLM-based systems, model serving, agentic workflows)
• Technical consulting or embedded SRE experience with cross-functional engineering teams