Azure ML Platform Engineer: MLOps & AI Infra

jci; Dallas (TX); On-site; USD 85,000 - 107,000; Full time

Benefits offered by this job

Competitive benefits package

Job summary

Johnson Controls International (JCI) is seeking a Machine Learning / Platform Engineer to join our AI and Data Platform team. You’ll build secure, scalable ML infrastructure on Azure, automate pipelines with Terraform and Azure DevOps, and enable enterprise-scale LLM inference and retraining across product and operations.

You’ll work at the intersection of ML, DevOps, and cloud engineering, delivering end-to-end pipelines, governance, and secure deployments while collaborating with data

Qualifications

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

Responsibilities

• 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 with a focus on automation, security, and scalability.
• Develop and manage infrastructure as code using Terraform, including provisioning compute clusters (e.g., AKS, AML compute).
• Implement model lifecycle management with Azure-native MLOps components.

Skills

Python

Azure

Terraform

Kubernetes

Docker

Azure DevOps

LangChain

Transformers

PyTorch

Education

Bachelor’s or Master’s in CS/Engineering

Tools

Azure ML

Azure DevOps Pipelines

AKS

Git

Job description

Johnson Controls International (JCI) is seeking a Machine Learning / Platform Engineer to join our AI and Data Platform team. You’ll build secure, scalable ML infrastructure on Azure, automate pipelines with Terraform and Azure DevOps, and enable enterprise-scale LLM inference and retraining across product and operations.

You’ll work at the intersection of ML, DevOps, and cloud engineering, delivering end-to-end pipelines, governance, and secure deployments while collaborating with data