{"id":1548759,"url":"https://alion.io/job/johnson-controls-platform-engineer","title":"Platform Engineer","company":{"id":448321,"name":"Johnson Controls","domain":"johnsoncontrols.com","url":"https://alion.io/company/johnsoncontrols-3","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":88,"open_postings":702,"ghost_share":0.001,"stale_share":0.477,"repost_share":0.004,"time_to_fill_p50_days":24,"computed_at":"2026-09-30T05:45:00Z"}},"role":"DevOps","role_family":"DevOps","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Monterrey, Mexico"],"countries":["MX"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":35000,"max_usd":90000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2013},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"Azure AKS","optional":false},{"name":"Azure DevOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Docker","optional":false},{"name":"FAISS","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Platform Engineering","optional":false},{"name":"PowerShell","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Redis","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Terraform","optional":false},{"name":"Transformers","optional":false},{"name":"LLMOps","optional":true}],"status":"live","first_seen_at":"2026-09-30T23:02:02Z","employer_posted_date":"2026-09-30","last_verified_at":"2026-09-30T23:02:01Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"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.\nFor 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.\nYou’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.\nHow you will do it\nML Platform Engineering & MLOps (Azure-Focused)\nBuild and manage end-to-end ML/LLM pipelines on Azure ML using Azure DevOps for CI/CD, testing, and release automation.\nOperationalize LLMs and generative AI solutions (e.g., GPT, LLaMA, Claude) with a focus on automation, security, and scalability.\nDevelop and manage infrastructure as code using Terraform, including provisioning compute clusters (e.g., Azure Kubernetes Service, Azure Machine Learning compute), storage, and networking.\nImplement robust model lifecycle management (versioning, monitoring, drift detection) with Azure-native MLOps components.\nInfrastructure & Cloud Architecture\nDesign highly available and performant serving environments for LLM inference using Azure Kubernetes Service (AKS) and Azure Functions or App Services.\nBuild and manage RAG pipelines using vector databases (e.g., Azure Cognitive Search, Redis, FAISS) and orchestrate with tools like LangChain or Semantic Kernel.\nEnsure security, logging, role-based access control (RBAC), and audit trails are implemented consistently across environments.\nAutomation & CI/CD Pipelines\nBuild reusable Azure DevOps pipelines for deploying ML assets (data pre-processing, model training, evaluation, and inference services).\nUse Terraform to automate provisioning of Azure resources, ensuring consistent and compliant environments for data science and engineering teams.\nIntegrate automated testing, linting, monitoring, and rollback mechanisms into the ML deployment pipeline.\nCollaboration & Enablement\nWork closely with Data Scientists, Cloud Engineers, and Product Teams to deliver production-ready AI features.\nContribute to solution architecture for real-time and batch AI use cases, including conversational AI, enterprise search, and summarization tools powered by LLMs.\nProvide technical guidance on cost optimization, scalability patterns, and high-availability ML deployments.\nQualifications & Skills\nRequired Experience\nBachelor’s or Master’s in Computer Science, Engineering, or a related field.\n5+ years of experience in ML engineering, MLOps, or platform engineering roles.\nStrong experience deploying machine learning models on Azure using Azure ML and Azure DevOps.\nProven experience managing infrastructure as code with Terraform in production environments.\nTechnical Proficiency\nProficiency in Python (PyTorch, Transformers, LangChain) and Terraform, with scripting experience in Bash or PowerShell.\nExperience with Docker and Kubernetes, especially within Azure (AKS).\nFamiliarity with CI/CD principles, model registry, and ML artifact management using Azure ML and Azure DevOps Pipelines.\nWorking knowledge of vector databases, caching strategies, and scalable inference architectures.\nSoft Skills & Mindset\nSystems thinker who can design, implement, and improve robust, automated ML systems.\nExcellent communication and documentation skills-capable of bridging platform and data science teams.\nStrong problem-solving mindset with a focus on delivery, reliability, and business impact.\nPreferred Qualifications\nExperience with LLMOps, prompt orchestration frameworks (LangChain, Semantic Kernel), and open-weight model deployment.\nExposure to smart buildings, IoT, or edge-AI deployments.\nUnderstanding of governance, privacy, and compliance concerns in enterprise GenAI use cases.\nCertification in Azure (e.g., Azure Solutions Architect, Azure AI Engineer, Terraform Associate) is a 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now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":24,"reasons":["conf:5","win:early","comp:brand"],"computed_at":"2026-10-01T04:25:29Z"},"pay":null,"html_url":"https://alion.io/job/johnson-controls-platform-engineer","json_url":"https://alion.io/job/johnson-controls-platform-engineer.json","meta":{"generated_at":"2026-10-01T04:25:29Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":3731,"day_limit":5000,"remaining_today":1269,"minute_limit":60,"resets_at":"2026-10-02T00:00:00Z"}}}