Own the full lifecycle of infrastructure, deployment systems, and AI operations
Design, build, and maintain CI/CD pipelines (GitHub Actions, GitLab CI/CD)
Deploy and manage cloud infrastructure on GCP using Terraform
Build and maintain data pipelines supporting ML and AI workflows
Design and operate AI-powered workflows, including LLM integrations and agents
Support tool orchestration, prompt/context management, and AI-enabled systems
Build internal automation to improve engineering productivity using AI
Implement containerized systems using Docker and Kubernetes
Monitor and optimize systems using tools like Datadog
Troubleshoot production issues across: Cloud infrastructure, CI/CD pipelines, Data pipelines, AI systems (latency, failures, reliability)
Partner with engineering, data, and product teams to productionize AI capabilities
Drive adoption of DevOps, AI Ops, and automation best practices

8+ years of experience in DevOps, cloud infrastructure, or platform engineering, or AI Ops within SaaS or cloud-based environments
Strong hands-on experience with: GCP (Cloud Run, BigQuery, etc.)
Terraform or similar IaC tools
CI/CD systems (GitHub Actions, GitLab CI/CD)
Docker and Kubernetes
Data pipelines and distributed systems
Experience working with AI systems, including: Deploying or supporting ML/LLM systems in production
AI-assisted engineering tools (Claude Code, Cursor, Codex, etc.)
Understanding of agent workflows or AI tooling ecosystems
Experience with monitoring, logging, and alerting systems (e.g., Datadog)
Strong scripting skills (Python, Bash, or similar)
Understanding of IAM, security, and cloud best practices
Ability to troubleshoot complex production issues across systems
Clear communication and collaboration skills
A bias toward ownership — you solve problems end-to-end
Bonus Points: Experience with agent frameworks (LangChain, LangGraph, CrewAI, etc.)
Experience building AI-powered automation or internal tooling
Experience with serverless and cloud-native architectures
Experience with ML/LLM lifecycle management (evaluation, monitoring, versioning)
Experience scaling infrastructure in a high-growth environment

Liminal

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