Platform Engineer

jci; San Pedro Garza García; On-site; MXN 1,100,000 - 1,600,000; Full time

Job summary

Johnson Controls is seeking an experienced ML Platform Engineer to design and operate end-to-end ML/LLM pipelines on Azure. You will implement a secure, scalable infrastrucure using Terraform and AKS, enabling rapid model deployment and real-time inference across enterprise AI offerings.

You will collaborate with data scientists, cloud engineers, and product teams to deliver production-ready AI features, while ensuring governance, cost optimization, and high availability across cloud

Qualifications

• Bachelor’s or Master’s in Computer Science, Engineering, or related field.
• 5+ years in ML engineering, MLOps, or platform engineering.
• Strong experience deploying ML on Azure using Azure ML and Azure DevOps.
• Proficient with Terraform in production environments.

Responsibilities

• Build end-to-end ML/LLM pipelines on Azure ML via Azure DevOps.
• Operationalize LLMs with focus on automation, security, scalability.
• Develop infrastructure as code with Terraform provisioning AKS, AML compute, storage.
• Design high-availability serving environments for LLM inference on AKS.
• Collaborate with data scientists and product teams to deliver AI features.

Skills

ML engineering

MLOps

Platform engineering

Python

Azure

CI/CD

Terraform

Docker

Kubernetes

LangChain

Semantic Kernel

Education

Bachelor’s or Master’s in Computer Science or Engineering

Tools

Azure ML

Azure DevOps

Terraform

AKS

LangChain

Semantic Kernel

PyTorch

Docker

Kubernetes

Job description

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.