Platform Engineer

YDU JC Air Cond & Ref Inc.- Dubai; San Pedro Garza García; Híbrido; MXN 900.000 - 1.300.000; Jornada completa

Descripción de la vacante

Johnson Controls in San Pedro Garza Garcia, Nuevo Leon, Mexico seeks a Platform Engineer to advance ML platform engineering on Azure. You will design and operate scalable ML pipelines, manage infrastructure as code, and collaborate with data scientists and cloud engineers to deploy secure AI solutions.

Ideal candidates have 5+ years in ML engineering or platform roles, strong Azure DevOps and Terraform experience, and hands-on Kubernetes/Docker expertise in AKS.

Formación

• Bachelor’s or Master’s in CS, Eng, or related field.
• 5+ years in ML engineering, MLOps, or platform engineering.
• Strong experience deploying ML models on Azure using Azure ML and Azure DevOps.
• Proficient with Terraform for IaC; scripting in Bash/PowerShell.
• Experience with Docker and Kubernetes in Azure (AKS).

Responsabilidades

• Build end-to-end ML pipelines on Azure ML with CI/CD via Azure DevOps.
• Operationalize LLMs and generative AI with secure, scalable infra.
• Design and manage IaC with Terraform for compute clusters and storage.
• Collaborate with data scientists, cloud engineers, and product teams.
• Implement monitoring, security RBAC, and governance across environments.

Conocimientos

Python

Terraform

Bash

PowerShell

Docker

Kubernetes

Azure

Azure ML

LangChain

Transformers

CI/CD

ML Ops

Educación

Bachelor’s or Master in CS/Engineering

Herramientas

Azure DevOps

AKS

Azure ML

Terraform

Descripción del empleo

## Platform EngineerApply: San Pedro Garza Garcia-Nuevo Leon-Mexico: Full time: Posted Today: WD30280171Johnson 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.