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. 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.
Skills Required
- Bachelor's or Master's in Computer Science, Engineering, or 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.
- Proficiency in Python (PyTorch, Transformers, LangChain) and Terraform; scripting in Bash or PowerShell.
- Experience with Docker and Kubernetes, especially Azure Kubernetes Service (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 (Azure Cognitive Search, Redis, FAISS).
- Excellent communication, documentation, and systems-thinking skills.
- Experience with LLMOps, prompt orchestration frameworks, and open-weight model deployment.
- Exposure to smart buildings, IoT, or edge-AI deployments.
- Understanding of governance, privacy, and compliance for enterprise GenAI.
- Azure or Terraform certifications (e.g., Azure Solutions Architect, Azure AI Engineer, Terraform Associate).
Johnson Controls Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Johnson Controls and has not been reviewed or approved by Johnson Controls.
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Retirement Support — Retirement support is positioned as a meaningful part of the package through employer 401(k) matching, repeatedly framed as a strong pillar of the overall rewards mix. The matching contribution is described with specific match levels in multiple places, reinforcing perceived value for long-term saving.
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Leave & Time Off Breadth — Time off is presented as comparatively robust, with multiple paid holiday categories, vacation time, and sick time described as generous or “amazing” in places. Paid time off breadth appears to be a consistent contributor to total rewards attractiveness beyond base pay.
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Flexible Benefits — Benefits are described as broad and customizable, spanning standard medical/dental/vision plus optional add-ons like pet insurance, identity protection, and legal support. Tuition reimbursement is repeatedly highlighted as a high-value option supporting professional development.
Johnson Controls Insights
What We Do
At Johnson Controls, we transform the environments where people live, work, learn and play. From optimizing building performance to improving safety and enhancing comfort, we drive the outcomes that matter most. Dedicated to protecting the environment, we deliver our promise in industries such as healthcare, education, data centers and manufacturing. With a global team of 100,000 experts in more than 150 countries and over 130 years of innovation, we are the power behind our customers’ mission. Our leading portfolio of building technology and solutions includes some of the most trusted names in the industry, such as Tyco®, York®, Metasys®, Ruskin®, Titus®, Frick®, Penn®, Sabroe®, Simplex®, Ansul® and Grinnell®.
