Cloud DevOps Engineer at Liminal Strategy – Salt Lake City, Utah

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 systemsDesign and operate CI/CD pipelines for fast, reliable releasesBuild and manage scalable infrastructure on GCP using TerraformImplement and support AI-powered workflows, including LLMs and agent-based systemsMonitor, 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 LikeIn Your First 30 DaysAudit existing infrastructure, CI/CD pipelines, and deployment workflowsUnderstand current data pipelines, ML/LLM usage, and AI workflowsIdentify reliability risks, bottlenecks, and gaps in automation and observabilityDocument system architecture and operational standardsPropose improvements to increase stability, speed, and AI system reliabilityIn Your First 90 DaysImprove CI/CD pipelines to enable faster, safer deploymentsDeploy and manage infrastructure using Terraform and GCP best practicesImplement monitoring and alerting across infrastructure and AI systemsSupport and productionize AI workflows and LLM-powered featuresReduce manual work through automation and reusable toolingIn Your First YearBuild a scalable, repeatable infrastructure and AI operations frameworkImprove uptime, deployment frequency, and system reliabilityEstablish DevOps and AI Ops best practices across engineeringEnable reliable deployment of AI systems and agent workflowsServe as the go-to expert for infrastructure, performance, and AI system operationsWhat You’ll DoOwn the full lifecycle of infrastructure, deployment systems, and AI operationsDesign, build, and maintain CI/CD pipelines (GitHub Actions, GitLab CI/CD)Deploy and manage cloud infrastructure on GCP using TerraformBuild and maintain data pipelines supporting ML and AI workflowsDesign and operate AI-powered workflows, including LLM integrations and agentsSupport tool orchestration, prompt/context management, and AI-enabled systemsBuild internal automation to improve engineering productivity using AIImplement containerized systems using Docker and KubernetesMonitor and optimize systems using tools like DatadogTroubleshoot production issues across:Cloud infrastructureCI/CD pipelinesData pipelinesAI systems (latency, failures, reliability)Partner with engineering, data, and product teams to productionize AI capabilitiesDrive adoption of DevOps, AI Ops, and automation best practicesWhat You Bring8+ years of experience in DevOps, cloud infrastructure, or platform engineering, or AI Ops within SaaS or cloud-based environmentsStrong hands-on experience with:GCP (Cloud Run, BigQuery, etc.)Terraform or similar IaC toolsCI/CD systems (GitHub Actions, GitLab CI/CD)Docker and KubernetesData pipelines and distributed systemsExperience working with AI systems, including:Deploying or supporting ML/LLM systems in productionAI-assisted engineering tools (Claude Code, Cursor, Codex, etc.)Understanding of agent workflows or AI tooling ecosystemsExperience with monitoring, logging, and alerting systems (e.g., Datadog)Strong scripting skills (Python, Bash, or similar)Understanding of IAM, security, and cloud best practicesAbility to troubleshoot complex production issues across systemsClear communication and collaboration skillsA bias toward ownership — you solve problems end-to-endBonus PointsExperience with agent frameworks (LangChain, LangGraph, CrewAI, etc.)Experience building AI-powered automation or internal toolingExperience with serverless and cloud-native architecturesExperience with ML/LLM lifecycle management (evaluation, monitoring, versioning)Experience scaling infrastructure in a high-growth environmentWhy 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