Ai/ML Engineer

Johnson Controls; Irving (TX); On-site; USD 85,000 - 107,000; Full time

Job summary

A leading global technology provider is seeking a Machine Learning / Platform Engineer to join their AI and Data Platform team. This role focuses on building scalable and automated infrastructure on Azure, developing ML/LLM pipelines, and collaborating with data scientists and engineers. Candidates should have 5+ years of experience, especially in MLOps, with strong skills in Python, Terraform, and Azure DevOps. The position offers a competitive salary range of $85,000 - $107,000 and benefits.

Qualifications

• 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.

Responsibilities

• Build and manage end-to-end ML/LLM pipelines on Azure ML using Azure DevOps for CI/CD.
• Develop and manage infrastructure as code using Terraform.
• Operationalize LLMs and generative AI solutions focusing on automation, security, and scalability.
• Design highly available environments for LLM inference using Azure Kubernetes Service.

Skills

ML engineering

MLOps

Platform engineering

Python

Terraform

Azure ML

Bash

PowerShell

Docker

Kubernetes

Education

Bachelor’s or Master’s in Computer Science, Engineering, or related field

Tools

Azure DevOps

Azure Kubernetes Service

Vector databases

Job description

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.
• 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.

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.
• 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.
• Integrate automated testing, linting, monitoring, and rollback mechanisms into the ML deployment pipeline.

Collaboration & Enablement

• Work closely with Data Scientists, Cloud Engineers, and Product Teams to deliver production-ready AI features.
• Contribute to solution architecture for real-time and batch AI use cases, including conversational AI, enterprise search, and summarization tools powered by LLMs.
• Provide technical guidance on cost optimization, scalability patterns, and high-availability ML deployments.

Qualifications & Skills

Required Experience

• Bachelor’s or Master’s in Computer Science, Engineering, or a related field.
• 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.

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).
• 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.

Soft Skills & Mindset

• Systems thinker who can design, implement, and improve robust, automated ML systems.
• Excellent communication and documentation skills—capable of bridging platform and data science teams.
• Strong problem-solving mindset with a focus on delivery, reliability, and business impact.

Preferred Qualifications

• Experience with LLMOps, prompt orchestration frameworks (LangChain, Semantic Kernel), and open-weight model deployment.
• Exposure to smart buildings, IoT, or edge-AI deployments.
• 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