DevOps Engineer, Infrastructure & Platforms
Ricursive Intelligence4 hours ago
Palo Alto, CA, USAMid Level
Responsibilities
- Design, build, and extend the self-managed cloud platform and infrastructure-as-code patterns using Terraform.
- Own day-to-day platform deployments across multiple environments, including performance, security, reliability, and scalability.
- Design, implement, and operate production CI/CD pipelines in GitHub Actions.
- Architect scalable AI tooling and developer experience workflows across multiple user environments.
- Build an observability stack covering pipeline health, application metrics, and ML workloads.
- Collaborate with physical design engineers and work within security and deployment restrictions.
Requirements
- Bachelor’s degree in computer science, computer engineering, electrical engineering, or a closely related technical field, or equivalent practical experience.
- At least 4 years of hands-on infrastructure or platform engineering experience with ownership of systems used by others.
- Experience owning production infrastructure-as-code architecture, including maintenance and new features.
- Experience designing and implementing production CI/CD pipelines that ship reproducible artifacts with attention to performance, scalability, and security.
- Experience with observability tooling for pipeline health and application-level metrics.
- Preferred: hands-on experience with GCP, Kubernetes, and GitHub Actions, including custom runner setups.
- Preferred: experience operating ML infrastructure for training and evaluation workloads using GPU or TPU compute.
- Preferred: familiarity with LLM observability, tracing, evaluations, and cost and latency monitoring.
- Preferred: security experience including dependency supply-chain hardening, OIDC-based authentication, least-privilege secrets, SOC 2, or penetration testing.
- Preferred: early-stage startup experience building infrastructure from zero or near-zero.
Tech Stack
Categories
About Ricursive Intelligence
Ricursive Intelligence builds AI systems that co-optimize machine learning models and the chips that run them, starting with AI-driven chip design tools for semiconductor and AI hardware teams. Its business focuses on software and research that closes the loop between model performance and silicon layout to accelerate development cycles. Privately held and founded in 2025, it operates at the intersection of AI infrastructure and semiconductor design.