AI Platform Engineer

Johnson Controls

. Build and operate secure, scalable AI landing zones and shared platform services, primarily on Azure and increasingly across AWS .

Posted 10/14/2026full-timeUnited StatesMid-LevelSenior💰 $125,000 - $145,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and operating secure, scalable AI platforms on Azure and AWS, with a strong focus on Terraform, Azure DevOps, and cloud networking. Proficient in implementing CI/CD processes, automated testing, and operational support for production services while ensuring compliance and security best practices.

Highest-signal resume keywords

Azure Solutions DevelopmentTerraform Module DevelopmentCI/CD ImplementationCloud Networking ExpertiseAI Platform Operations

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

TerraformAzure DevOpsAzure AI FoundryAzure Machine LearningAWS SageMakerPythonPowerShellBashContainerizationAPI Management

Soft Skills

Clear CommunicationDocumentation SkillsCollaboration

Tools & Technologies

AzureAWSDockerKubernetesAzure API ManagementAzure Container AppsAzure FunctionsAzure Key VaultAzure Cosmos DBAzure Application Insights

Certifications & Qualifications

Azure AdministratorAzure Solutions ArchitectAzure DevOps EngineerAzure AI EngineerAWS Solutions ArchitectTerraform Associate

Industry Keywords

Cloud EngineeringDevOpsMLOpsInfrastructurePlatform EngineeringAI GovernanceIncident ManagementService ReliabilityCost ManagementMulti-Cloud

Tech Stack

Tools & technologies

AWSAzureCloudCyber SecurityDNSDockerKubernetesPythonRedisServiceNowTerraformVault

About the role

Key responsibilities & impact

Build and operate secure, scalable AI landing zones and shared platform services, primarily on Azure and increasingly across AWS
Develop reusable infrastructure modules and deployment patterns using Terraform and Terragrunt across development, test, and production environments
Provision and support Azure AI Foundry, Azure Machine Learning, Azure OpenAI, AI Search, Document Intelligence, Container Apps, App Services, Function Apps, Key Vault, Azure Container Registry, Storage, Cosmos DB, Redis, Application Insights, Log Analytics, and supporting services
Contribute to AWS AI/ML platform capabilities using Amazon Bedrock, SageMaker, API Gateway, Lambda, S3, IAM, and associated networking and security services
Build and operate governed access to foundation models and AI services through Azure API Management and equivalent cloud gateway patterns
Implement authentication, authorization, secrets management, rate limiting, quotas, routing, retries, circuit breakers, usage tracking, and cost controls
Support model deployments, upgrades, endpoint lifecycle management, capacity planning, and controlled access for consuming teams
Enable secure hosting patterns for APIs, agents, containerized workloads, scheduled jobs, and event-driven services
Create and maintain Azure DevOps pipelines for infrastructure deployment, validation, policy checks, application hosting, and controlled releases
Build automated testing for Terraform modules, platform services, networking, security controls, and deployment smoke tests
Create sample projects, templates, reference implementations, runbooks, and onboarding documentation
Troubleshoot platform access, networking, identity, certificates, private endpoints, deployment, and runtime issues with consuming teams
Implement private networking, managed identities, Entra ID integration, RBAC, least-privilege access, secrets management, logging, auditability, and secure-by-default configurations
Partner with Cybersecurity, Cloud Engineering, Networking, Enterprise Architecture, AI Governance, and service owners to move solutions into supported production environments
Build dashboards, alerts, health checks, monitoring, and operational metrics for platform availability, model usage, cost, capacity, and service quality
Participate in incident response, root-cause analysis, problem management, platform maintenance, change management, and continuous improvement
Support platform request, approval, provisioning, documentation, and handover processes
Collaborate with AI engineers, data scientists, solution architects, product teams, and cloud specialists to translate use cases into reusable platform capabilities
Contribute to architecture reviews, technical design decisions, backlog refinement, sprint delivery, code reviews, and platform roadmap planning
Promote automation, reliability, security, maintainability, cost awareness, and clear technical documentation

Requirements

What you’ll need

Bachelor's degree in Computer Science, Engineering, Information Technology, or a related discipline, or equivalent practical experience
5+ years of experience in cloud, DevOps, platform, SRE, infrastructure, or MLOps engineering roles
Hands-on experience building production Azure solutions using Terraform and Azure DevOps
Strong knowledge of cloud networking, private endpoints, DNS, identity, RBAC, managed identities, secrets, and secure service-to-service connectivity
Experience with containerized and managed application platforms such as Azure Container Apps, App Services, Functions, Docker, or Kubernetes
Experience implementing CI/CD, automated testing, deployment controls, monitoring, alerting, and operational support for production services
Proficiency in at least one scripting or programming language such as Python, PowerShell, or Bash
Ability to diagnose complex infrastructure, networking, authentication, certificate, deployment, and runtime issues
Clear communication and documentation skills, with the ability to work across engineering, architecture, security, governance, and product teams
Preferred: experience with Azure AI Foundry, Azure OpenAI, Azure Machine Learning, Azure AI Search, Document Intelligence, or similar managed AI services
Preferred: experience designing or operating API gateways using Azure API Management
Preferred: experience developing reusable Terraform modules and working with Terragrunt
Preferred: experience with enterprise landing zones, hub-and-spoke networking, private cloud services, policy enforcement, and multi-subscription or multi-account environments
Preferred: experience operating AI or ML platforms
Preferred: working knowledge of cloud cost management, service reliability, incident management, and platform product thinking
Nice to have: experience with AWS platform engineering, including Bedrock, SageMaker, API Gateway, IAM, Lambda, S3, VPC networking, Terraform, or Terragrunt
Nice to have: experience with multi-cloud AI gateway, model-routing, or centralized model-access patterns
Nice to have: knowledge of LLMOps, agent orchestration, MCP, RAG, embeddings, vector databases, model evaluation, safety, or observability patterns
Nice to have: experience with OpenTelemetry, SIEM integration, dashboards, load testing, resilience testing, or performance engineering
Nice to have: exposure to ServiceNow-based request and fulfilment processes, CMDB, application onboarding, or enterprise architecture approval processes
Relevant certifications are nice to have, including Azure Administrator, Azure Solutions Architect, Azure DevOps Engineer, Azure AI Engineer, AWS Solutions Architect, or Terraform Associate
Nice to have: experience supporting global teams and services across multiple regions, time zones, and regulatory environments

Benefits

Comp & perks

Competitive benefits package