AI Platform Engineer

• 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, with consistent configuration 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 services such as Amazon Bedrock, SageMaker, API Gateway, Lambda, S3, IAM, and associated networking and security services.
• Design platform services so new capabilities can be onboarded through repeatable modules, documented standards, and approved deployment patterns.

AI Gateways, Model Access & Runtime 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 for model and API consumption.
• Support pay-as-you-go and provisioned-throughput model deployments, model 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 using managed cloud platforms.

Automation, CI/CD & Developer Enablement

• 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 that help teams adopt the platform safely and quickly.
• Work with consuming teams to troubleshoot platform access, networking, identity, certificates, private endpoints, deployment, and runtime issues.

Security, Governance, Reliability & Operations

• 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 from development 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, ensuring solutions remain supportable and compliant.

Collaboration & Engineering Standards

• 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 engineering practices focused on automation, reliability, security, maintainability, cost awareness, and clear technical documentation.

• 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

• Hands-on experience with Azure AI Foundry, Azure OpenAI, Azure Machine Learning, Azure AI Search, Document Intelligence, or similar managed AI services.
• Experience designing or operating API gateways using Azure API Management, including policies for authentication, routing, throttling, retries, observability, and usage controls.
• Experience developing reusable Terraform modules and working with Terragrunt, remote state, module versioning, and environment promotion patterns.
• Experience with enterprise landing zones, hub-and-spoke networking, private cloud services, policy enforcement, and multi-subscription or multi-account environments.
• Experience operating AI or ML platforms, including model endpoints, capacity, quotas, evaluation services, vector search, RAG infrastructure, or agent runtimes.
• 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.
• Experience with multi-cloud AI gateway, model-routing, or centralized model-access patterns.
• Knowledge of LLMOps, agent orchestration, MCP, RAG, embeddings, vector databases, model evaluation, safety, or observability patterns.
• Experience with OpenTelemetry, SIEM integration, dashboards, load testing, resilience testing, or performance engineering.
• Exposure to ServiceNow-based request and fulfilment processes, CMDB, application onboarding, or enterprise architecture approval processes.
• Relevant certifications such as Azure Administrator, Azure Solutions Architect, Azure DevOps Engineer, Azure AI Engineer, AWS Solutions Architect, or Terraform Associate.
• Experience supporting global teams and services across multiple regions, time zones, and regulatory environments.

Mindset

• Platform-first engineer who looks for reusable solutions rather than one-off deployments.
• Security-conscious and comfortable balancing delivery speed with enterprise controls.
• Operationally minded, with a focus on reliability, supportability, automation, and continuous improvement.
• Pragmatic problem solver who can work through ambiguity and evolving cloud and AI technologies.

Johnson Controls, a global leader in thermal management, mission-critical building systems, energy efficiency, and decarbonization, helps customers use energy more productively, reduce carbon emissions, and operate with the precision and resilience required in rapidly expanding industries such as data centers, healthcare, pharmaceuticals, advanced manufacturing, and higher education.

For more than 140 years, Johnson Controls has delivered performance where it really matters. Backed by advanced technology, lifecycle services and an industry-leading field organization, we elevate customer performance, turn goals into real-world results and help move society forward.

Johnson Controls International (JCI) is looking for an AI Platform Engineer to join the AI Engineering & Platforms team. This role focuses on building, automating, securing, and operating the enterprise landing zones and shared services that enable AI, machine learning, generative AI, and agent-based solutions across JCI.

You will work across cloud platform engineering, infrastructure as code, DevOps, networking, security, observability, and service operations. The role is platform-first: creating reusable capabilities, governed deployment patterns, and reliable self-service experiences for AI engineering, data science, product, and business teams.