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Johnson Controls

Provides smart building technology and services

AI/ML Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeDallas, TX, USA$85k - $107kIn-person

About the Job

Requirements

  • A bachelor's or master's degree in Computer Science, Engineering, or a related field is required.
  • At least 5 years of experience in machine learning engineering, MLOps, or platform engineering roles is required.
  • Strong experience deploying machine learning models on Azure using Azure Machine Learning and Azure DevOps is required.
  • Production experience managing infrastructure as code with Terraform is required.
  • Proficiency in Python, PyTorch, Transformers, LangChain, and Terraform is required, along with scripting experience in Bash or PowerShell.
  • Experience with Docker and Kubernetes, especially in Azure using Azure Kubernetes Service, is required.
  • Familiarity with continuous integration and continuous delivery principles, model registries, and machine learning artifact management using Azure Machine Learning and Azure DevOps Pipelines is required.
  • Working knowledge of vector databases, caching strategies, and scalable inference architectures is required.
  • Strong systems-thinking and problem-solving abilities, communication and documentation skills, and a focus on delivery, reliability, and business impact are required.

Responsibilities

  • Build and manage end-to-end machine learning and large language model pipelines on Azure Machine Learning, using Azure DevOps for continuous integration, testing, and release automation.
  • Operationalize large language models and generative AI solutions, including GPT, LLaMA, and Claude, with a focus on automation, security, and scalability.
  • Develop and manage infrastructure as code using Terraform, including provisioning compute clusters, storage, and networking.
  • Implement model lifecycle management, including versioning, monitoring, and drift detection, using Azure-native MLOps components.
  • Design highly available and performant serving environments for large language model inference using Azure Kubernetes Service and Azure Functions or App Services.
  • Build and manage retrieval-augmented generation pipelines using vector databases such as Azure Cognitive Search, Redis, or FAISS, and orchestrate them with tools such as LangChain or Semantic Kernel.
  • Implement security, logging, role-based access control, and audit trails consistently across environments.
  • Build reusable Azure DevOps pipelines to deploy machine learning assets for data preprocessing, model training, evaluation, and inference services.
  • Automate Azure resource provisioning with Terraform to ensure consistent and compliant environments for data science and engineering teams.
  • Integrate automated testing, linting, monitoring, and rollback mechanisms into the machine learning deployment pipeline.
  • Work 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 large language model-powered summarization tools.
  • Provide technical guidance on cost optimization, scalability patterns, and high-availability machine learning deployments.

Desired Qualifications

  • Experience with LLMOps, prompt orchestration frameworks such as LangChain and Semantic Kernel, and open-weight model deployment.
  • Exposure to smart buildings, Internet of Things, or edge-AI deployments.
  • Understanding of governance, privacy, and compliance concerns in enterprise generative AI use cases.
  • Azure certifications, such as Azure Solutions Architect or Azure AI Engineer, or Terraform Associate certification.

About the Company

Johnson Controls

Provides smart building technology and services

Johnson Controls transforms environments where people live, work, learn, and play by providing smart, healthy, and sustainable buildings. It offers comprehensive digital solutions through its OpenBlue platform, serving healthcare, education, data centers, airports, stadiums, manufacturing, and government sectors with a portfolio of building technology, software, and services focused on efficiency, safety, and sustainability. The company operates in more than 150 countries with about 100,000 experts and aims to reimagine building performance to benefit people, places, and the planet.

Company size
10,001+
Company stage
IPO
Headquarters
Cork, Ireland
Founded
1885

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