ML Engineer

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. Johnson Controls: Enhancing the Intelligence of Buildings Your buildings have a purpose. They are places for people to live or work. Facilities for learning or healing. Venues for entertainment and shopping. Sites for the specialized storage of tangible goods or mission-critical data. Your buildings have a huge variety of functions; they are central to your mission. This is where Johnson Controls comes in, helping drive the outcomes that matter most. Through a full range of systems and digital solutions, we make your buildings smarter. A smarter building is safer, more comfortable, more efficient, and, ultimately, more sustainable. Most important, smarter buildings let you focus more intensely on your unique mission. Better for your people. Better for your bottom line. Better for the planet. At Johnson Controls, we’ve been making buildings smarter since 1885, and our capabilities, depth of innovation experience, and global reach have been growing ever since. Today, we offer the world’s largest portfolio of building products, technologies, software, and services; we put that portfolio to work to transform the environments where people live, work, learn and play.