Cloud DevOps Engineer

Mastronardi Produce•Livonia, MI

2d•Onsite

About The Position

Mastronardi Produce is seeking a DevOps Cloud Engineer for their corporate office in Livonia, Michigan. This role is responsible for the full lifecycle of the company's cloud infrastructure, DevOps practices, and AI platform operations. The engineer will provision, secure, deploy, integrate, monitor, and scale Azure infrastructure and AI systems. This hands-on, individual contributor role involves writing code and scripts, configuring infrastructure, deploying and operating AI systems, and managing them in production. The position aims to provide redundancy for critical cloud and AI platform operations and build the operational backbone for scaling automation and AI investments safely. The role requires adherence to Mastronardi's PRIDE values: Passion, Respect, Innovation, Drive, and Excellence.

Requirements

Bachelor’s degree in computer science, information systems, or a related field.
Microsoft Azure: deploying and managing VMs, networking, storage, and identity using infrastructure-as-code (ARM/Bicep, Terraform, or equivalent).
CI/CD: building and maintaining pipelines (Azure DevOps, GitHub Actions, or equivalent) for infrastructure and application deployments.
Hands-on experience with Docker required.
Scripting/programming experience (PowerShell, Python, or similar) for infrastructure automation and custom integrations.
Experience with Microsoft Entra ID, RBAC, network security groups, and related access-control practices.
Experience with Azure Monitor, Log Analytics, Grafana, or equivalent for production infrastructure.
Experience with backup, disaster recovery, and patching practices for production infrastructure.
Experience with cloud cost management and optimization practices (Azure Cost Management or equivalent).
Ability to query, join, and filter data across relational databases (SQL).
Hands-on experience with Make.com (or a comparable integration/automation platform such as Zapier or n8n), including building, debugging, and maintaining production scenarios.
Experience deploying and operating AI platforms such as Azure AI Foundry — connecting agents and models to production systems and data.
Understanding of agentic AI and multi-step autonomous workflows, and how to operate and monitor them reliably in production.
Experience building and consuming REST APIs, webhooks, and system-to-system integrations connecting AI platforms to enterprise systems.
Experience building and maintaining automated workflows with Microsoft Power Automate and Power Platform.
Understanding of AI governance and security practices — guardrails, access control, and auditability for production AI systems.

Nice To Haves

Kubernetes/AKS or Azure Container Apps experience.
ERP or enterprise systems experience (Microsoft Dynamics NAV/365, SAP, or equivalent).
Familiarity with LLM observability concepts — tracking latency, token usage/cost, and evaluating agent or model output quality in production.
Exposure to MLOps/LLMOps concepts (model and prompt versioning, safe rollout practices).

Responsibilities

Deploy and manage Azure infrastructure (VMs, networking, storage, identity) using infrastructure-as-code (ARM/Bicep, Terraform, or equivalent).
Design, build, and maintain CI/CD pipelines (Azure DevOps, GitHub Actions, or equivalent) for infrastructure changes, automations, and AI platform deployments.
Build and maintain containerized workloads (Docker) and orchestration (Kubernetes/AKS or Azure Container Apps) for infrastructure and AI services.
Implement configuration management and patching practices.
Design and maintain networking, identity, and access controls (Microsoft Entra ID, RBAC, network security groups).
Build monitoring, logging, and alerting (Azure Monitor, Log Analytics, or equivalent).
Own backup, disaster recovery, and business continuity procedures.
Manage cloud cost and capacity planning, right-sizing resources and identifying optimization opportunities.
Participate in an on-call/incident response rotation, performing root-cause analysis and driving remediation.
Cross-train other Cloud Operations & Infrastructure team members.
Deploy, configure, and operate Azure AI Foundry agents, models, and endpoints.
Build, maintain, and troubleshoot automation scenarios in Make.com (or comparable platforms).
Build and operate the infrastructure and integration layer for AI Enablement's agentic workflows.
Implement observability specific to AI workloads — tracking latency, token usage/cost, and failure rates.
Manage versioning and rollout of AI models, prompts, and agent configurations.
Implement guardrails and access controls around AI platforms and agents.
Build and maintain integrations and connectors between Power Automate, Make.com, APIs, and enterprise systems.
Support scaling of AI inference and automation workloads.
Partner with the AI Enablement team to take AI solutions from build to production operation.
Evaluate and recommend new automation, orchestration, and AI infrastructure tooling.
Test infrastructure changes, automations, and AI deployments thoroughly before release.
Maintain a library of reusable scripts, templates, and automation patterns.
Create and maintain clear documentation for all infrastructure, automations, AI deployments, and operating procedures.
Ensure all work aligns with IT governance, security, and compliance standards.