Liminal is the actionable intelligence company. We've built a proprietary Living Graph — a verified knowledge architecture that maps the real-time structure of Identity, Fraud, and Cybersecurity — and the agentic AI systems and human verification layer to make it trustworthy. Visa, Mastercard, Google, and JPMC use it to make strategic and revenue decisions. Series A, 80 people, offices in NYC, Salt Lake City, Porto, Lisbon, and Manila. The architecture works, the customers are real, and we're scaling the team.

The Role

We’re looking for a Cloud DevOps & AI Ops Engineer who fully owns the infrastructure and operational lifecycle for our platform — from code deployment to production AI systems. You take end-to-end responsibility for how systems are built, deployed, scaled, and maintained in production.

This is not a maintenance-only role. You will:

• Diagnose issues across cloud infrastructure, data pipelines, and AI systems
• Design and operate CI/CD pipelines for fast, reliable releases
• Build and manage scalable infrastructure on GCP using Terraform
• Implement and support AI-powered workflows, including LLMs and agent-based systems
• Monitor, debug, and optimize production systems across infrastructure and AI workloads

You are both the infrastructure architect and the hands-on engineer, ensuring our systems — including AI — run reliably in production.

This is a high-impact hire. You’ll define how infrastructure and AI systems operate at scale — establishing best practices, building automation, and shaping how engineering teams leverage AI in production.

This role is based in Salt Lake City and reports to the VP of Engineering.

What Success Looks Like

In Your First 30 Days

• Audit existing infrastructure, CI/CD pipelines, and deployment workflows
• Understand current data pipelines, ML/LLM usage, and AI workflows
• Identify reliability risks, bottlenecks, and gaps in automation and observability
• Document system architecture and operational standards
• Propose improvements to increase stability, speed, and AI system reliability

In Your First 90 Days

• Improve CI/CD pipelines to enable faster, safer deployments
• Deploy and manage infrastructure using Terraform and GCP best practices
• Implement monitoring and alerting across infrastructure and AI systems
• Support and productionize AI workflows and LLM-powered features
• Reduce manual work through automation and reusable tooling

In Your First Year

• Build a scalable, repeatable infrastructure and AI operations framework
• Improve uptime, deployment frequency, and system reliability
• Establish DevOps and AI Ops best practices across engineering
• Enable reliable deployment of AI systems and agent workflows
• Serve as the go-to expert for infrastructure, performance, and AI system operations

What You’ll Do

• 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

What You Bring

• 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

Why Liminal

You own how systems run in production. This isn’t a support role — you are responsible for how infrastructure, deployments, and AI systems operate at scale.

You’ll build the foundation, not just maintain it. You’ll define how DevOps and AI Ops are done — from CI/CD to AI workflows to system reliability.

You’ll shape how AI is used across engineering. Every workflow, tool, and system you build directly impacts how teams ship, automate, and scale.

You’ll work on real systems at scale. Your work directly impacts the performance, reliability, and evolution of both our platform and AI capabilities.

Compensation

$180,000–$210,000 USD base salary, plus equity and a performance bonus tied to company-wide revenue share.

Location: Salt Lake City, UT (hybrid)

Our Process

We respect your time. Here's what to expect:

Recruiter Screen → Hiring Manager Interview → Behavioral Interview → Practical Interview → CEO Interview → Offer