Senior Cloud & AI Engineer

Prologis•Denver, CO

4d•$121,000 - $159,000•Onsite

About The Position

We are seeking a senior cloud engineer who combines deep infrastructure engineering expertise with an AI-first mindset. This role will help transform how Cloud Engineering operates — replacing repetitive work with intelligent automation, agentic workflows, and self-service platforms. The right candidate will not simply automate existing tasks; they will challenge current ways of working, redesign operational processes, and build scalable patterns that improve reliability, speed, and engineering leverage across the team. This is a change-agent role. You will work at the intersection of cloud infrastructure, platform engineering, and applied AI — evaluating where agentic automation can replace manual toil, where deterministic automation is the right answer, and how to combine the two safely at enterprise scale.

Requirements

Strong experience with AWS (EC2, Lambda, RDS, S3, DynamoDB, IAM) and Azure.
Solid networking fundamentals (DNS, routing, security).
Infrastructure as Code — Terraform preferred, CloudFormation experience a plus.
CI/CD pipelines (Azure DevOps or similar).
Strong scripting and programming — Python preferred; Bash, PowerShell.
Experience with Git workflows (branching, pull requests, code review).
Experience with authentication and identity systems (SAML, OAuth, Entra ID / Azure AD).
Experience designing or building AI-assisted or agentic workflows for engineering or operational use cases.
Ability to evaluate when AI is the right tool, where deterministic automation is better, and how to combine the two safely.
Demonstrated ability to apply AI tooling to reduce operational overhead — not just using copilots, but building AI into systems and processes.
Familiarity with AI orchestration concepts: tool use, context management, human-in-the-loop patterns, observability for AI-driven actions.
Strong troubleshooting instincts across distributed systems and APIs.
Ability to identify inefficiencies and drive systemic change — not just automate what exists, but question whether it should exist.
Ability to influence engineering teams and introduce new patterns, standards, and ways of working.

Nice To Haves

7+ years in infrastructure, DevOps, cloud engineering, or platform engineering.
Experience with agentic AI frameworks, LLM orchestration, or AI-driven workflow tools (e.g., OpenAI Agents SDK, LangChain, n8n, MCP).
Experience with internal developer platforms, self-service tooling, or platform engineering practices.
Familiarity with ServiceNow, or similar ITSM/workflow platforms.
Experience supporting production systems in enterprise environments.

Responsibilities

Identify high-friction operational workflows and redesign them using AI, automation, and platform engineering patterns.
Build agentic and event-driven workflows for provisioning, remediation, change execution, and support deflection.
Establish engineering standards for safe AI use in cloud operations — including observability, human-in-the-loop controls, auditability, and rollback.
Implement AI-assisted tooling for incident detection, classification, root cause analysis, and automated remediation.
Demonstrate willingness and capability to leverage emerging technology, automation, and AI tools to improve efficiency, quality, and speed. Exercises sound judgment, creative thinking, and accountability for outcomes.
Design and build self-service infrastructure capabilities using standardized modules, golden paths, and reusable components.
Enable teams to provision, manage, and operate infrastructure through code, APIs, and internal platforms — reducing dependency on manual requests and ticket-driven workflows.
Design and operate infrastructure across AWS and Azure using infrastructure-as-code (Terraform, CloudFormation).
Build scalable systems using serverless, event-driven, and cloud-native architectures.
Automate identity and access workflows (Entra ID, SAML/OAuth, Cognito).
Troubleshoot complex issues across cloud, networking, identity, and distributed systems.
Challenge existing operational processes and propose improvements that increase engineering leverage.
Influence engineering teams by introducing new patterns, standards, and ways of working.
Measure and communicate the impact of automation and AI adoption on operational metrics.