Ai/ML Engineer

Technical Proficiency - Proficiency in Python (PyTorch, Transformers, LangChain) and Terraform, with scripting experience in Bash or PowerShell. - Experience with Docker and Kubernetes, especially within Azure (AKS).

Johnson Controls International (JCI) is looking for a Machine Learning / Platform Engineer to join our growing AI and Data Platform team. This role is pivotal in enabling enterprise-scale ML and generative AI capabilities by building secure, scalable, and automated infrastructure on Azure using Terraform and Azure DevOps. You’ll work at the intersection of ML, DevOps, and cloud engineering—building the foundation that supports real-time LLM inference, retraining, orchestration, and integration across JCI’s product and operations landscape.

How you will do it ML Platform Engineering & MLOps (Azure-Focused) - Build and manage end-to-end ML/LLM pipelines on Azure ML using Azure DevOps for CI/CD, testing, and release automation.

ML Platform Engineering & MLOps (Azure-Focused) - Build and manage end-to-end ML/LLM pipelines on Azure ML using Azure DevOps for CI/CD, testing, and release automation. - Operationalize LLMs and generative AI solutions (e.g., GPT, LLaMA, Claude) with a focus on automation, security, and scalability.

- Operationalize LLMs and generative AI solutions (e.g., GPT, LLaMA, Claude) with a focus on automation, security, and scalability. - Develop and manage infrastructure as code using Terraform, including provisioning compute clusters (e.g., Azure Kubernetes Service, Azure Machine Learning compute), storage, and networking. - Implement robust model lifecycle management (versioning, monitoring, drift detection) with Azure-native MLOps components.

- Develop and manage infrastructure as code using Terraform, including provisioning compute clusters (e.g., Azure Kubernetes Service, Azure Machine Learning compute), storage, and networking. - Implement robust model lifecycle management (versioning, monitoring, drift detection) with Azure-native MLOps components. Infrastructure & Cloud Architecture

Infrastructure & Cloud Architecture - Design highly available and performant serving environments for LLM inference using Azure Kubernetes Service (AKS) and Azure Functions or App Services. - Build and manage RAG pipelines using vector databases (e.g., Azure Cognitive Search, Redis, FAISS) and orchestrate with tools like LangChain or Semantic Kernel.

- Design highly available and performant serving environments for LLM inference using Azure Kubernetes Service (AKS) and Azure Functions or App Services. - Build and manage RAG pipelines using vector databases (e.g., Azure Cognitive Search, Redis, FAISS) and orchestrate with tools like LangChain or Semantic Kernel. - Ensure security, logging, role-based access control (RBAC), and audit trails are implemented consistently across environments.

Automation & CI/CD Pipelines - Build reusable Azure DevOps pipelines for deploying ML assets (data pre-processing, model training, evaluation, and inference services). - Use Terraform to automate provisioning of Azure resources, ensuring consistent and compliant environments for data science and engineering teams.

- Build reusable Azure DevOps pipelines for deploying ML assets (data pre-processing, model training, evaluation, and inference services). - Use Terraform to automate provisioning of Azure resources, ensuring consistent and compliant environments for data science and engineering teams. - Integrate automated testing, linting, monitoring, and rollback mechanisms into the ML deployment pipeline.

- 5+ years of experience in ML engineering, MLOps, or platform engineering roles. - Strong experience deploying machine learning models on Azure using Azure ML and Azure DevOps. - Proven experience managing infrastructure as code with Terraform in production environments.

- Proficiency in Python (PyTorch, Transformers, LangChain) and Terraform, with scripting experience in Bash or PowerShell. - Experience with Docker and Kubernetes, especially within Azure (AKS). - Familiarity with CI/CD principles, model registry, and ML artifact management using Azure ML and Azure DevOps Pipelines.

- Experience with Docker and Kubernetes, especially within Azure (AKS). - Familiarity with CI/CD principles, model registry, and ML artifact management using Azure ML and Azure DevOps Pipelines. - Working knowledge of vector databases, caching strategies, and scalable inference architectures.

- Understanding of governance, privacy, and compliance concerns in enterprise GenAI use cases. - Certification in Azure (e.g., Azure Solutions Architect, Azure AI Engineer, Terraform Associate) is a plus. HIRING SALARY RANGE: $85,000 - 107,000 (Salary to be determined by the education, experience, knowledge, skills, and abilities of the applicant, internal equity, location and alignment with market data.) This position includes a competitive benefits package. For details, please visit the About Us tab on the Johnson Controls Careers site at https://jobs.johnsoncontrols.com/about-us

- Strong experience deploying machine learning models on Azure using Azure ML and Azure DevOps. - Proven experience managing infrastructure as code with Terraform in production environments. Technical Proficiency