Johnson Controls, a general 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.
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📌 Platform Engineer (San Pedro Garza García)
🏢 JOHNSON CONTROLS
📍 San Pedro Garza García