Cloud DevOps Engineer

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