Seniorr AI/ML Platform Engineer

Johnson Controls•Glendale, AZ

3d•Hybrid

About The Position

Build your best future with the Johnson Controls team As a global leader in smart, healthy and sustainable buildings, our mission is to reimagine the performance of buildings to serve people, places and the planet. Join a winning team that enables you to build your best future! Our teams are uniquely positioned to support a multitude of industries across the globe. You will have the opportunity to develop yourself through meaningful work projects and learning opportunities. We strive to provide our employees with an experience, focused on supporting their physical, financial, and emotional wellbeing. Become a member of the Johnson Controls family and thrive in an empowering company culture where your voice and ideas will be heard – your next great opportunity is just a few clicks away! What we offer: Competitive salary and bonus plan Paid vacation/holidays/sick time Comprehensive benefits package including 401K, medical, dental, and vision care On the job/cross training opportunities Encouraging and collaborative team environment Dedication to safety through our Zero Harm policy What you will do: Johnson Controls International (JCI) is seeking a Senior ML Platform Engineer to drive the strategy, architecture, and delivery of our next-generation AI and Machine Learning platforms. This senior role is responsible for designing scalable, secure, and production-grade ML/LLM infrastructure on Azure, enabling enterprise-wide adoption of advanced AI and generative AI capabilities. As a senior technical leader, you will shape platform standards, influence architectural direction, and mentor engineering teams while working across real-time inference, automation, and large-scale distributed systems. How you will do it: ML Platform Architecture & MLOps Leadership (Azure-Focused) Architect, lead, and optimize complex ML/LLM pipelines on Azure ML, defining best practices for CI/CD, testing automation, and secure deployment across environments. Drive the operationalization of large-scale LLMs (GPT, LLaMA, Claude, etc.), ensuring maintainability, observability, and model lifecycle efficiency. Define Terraform based IaC patterns and oversee provisioning of compute clusters (AKS, AML Compute), networking, and storage for ML workloads. Establish governance, drift monitoring, and enterprise grade model management practices. Architect high availability, low latency inference environments using Azure Kubernetes Service, serverless compute (Azure Functions), and distributed microservice patterns. Design and scale advanced RAG pipelines using vector databases such as Azure Cognitive Search, Redis, or FAISS, integrated with frameworks like Microsoft Agent Framework. Set standards for Azure security (RBAC, identity, audit), logging, observability, and compliance across ML/AI environments. Automation, CI/CD, and Platform Reliability Lead the creation of reusable, enterprise-wide Azure DevOps CI/CD templates for ML assets, ensuring consistency across data science teams. Champion automated testing, gating, rollback strategies, quality controls, and monitoring for ML service deployments. Establish platform wide SRE practices for ML systems, including reliability, scalability, and cost optimization frameworks. Partner with Data Science, Cloud Architecture, and Product teams to deliver highly impactful AI solutions that scale globally. Provide architectural guidance for real-time AI, conversational AI, enterprise search, analytics, and summarization workloads powered by LLMs. Mentor ML engineers and advocate for engineering excellence, design reviews, and knowledge-sharing across teams. Influence JCI’s AI platform roadmap, standards, and long-term architectural strategy.

Requirements

Bachelor’s or Master’s in Computer Science, Engineering, or a related field.
10+ years of experience in ML engineering, platform engineering, data engineering, or cloud architecture roles.
Deep experience deploying and scaling ML systems on Azure, including Azure ML, AKS, Azure DevOps, and distributed compute.
Significant hands-on experience with Terraform and cloud-native architectural patterns at enterprise scale.
Proven background in building secure, automated ML/LLM systems and managing full model lifecycles in production.
Expert proficiency in Python (PyTorch, Transformers, LangChain), Terraform, and Bash/PowerShell.
Advanced experience with Docker, Kubernetes, and GPU-accelerated inference on AKS.
Strong understanding of scalable inference patterns, LLM optimization, vector databases, and RAG architectures.
Deep knowledge of CI/CD, model registries, artifact management, and ML observability tooling.
Strategic thinker who can influence platform direction and drive architectural decisions.
Strong leadership, documentation, communication, and stakeholder management skills.
Ability to balance innovation, reliability, security, and cost to deliver business value at scale.
Demonstrated experience mentoring engineers and guiding cross-functional technical teams.

Nice To Haves

Expertise in LLMOps, prompt orchestration frameworks (LangChain, Microsoft Agent Framework), and custom model deployment (open-weights, fine-tuning, quantization).
Experience with IoT, smart building systems, or edge AI pipelines.
Knowledge of enterprise governance, AI compliance, and responsible AI practices.
Azure certifications (Azure Solutions Architect, Azure AI Engineer) and Terraform Associate.

Responsibilities

Architect, lead, and optimize complex ML/LLM pipelines on Azure ML, defining best practices for CI/CD, testing automation, and secure deployment across environments.
Drive the operationalization of large-scale LLMs (GPT, LLaMA, Claude, etc.), ensuring maintainability, observability, and model lifecycle efficiency.
Define Terraform based IaC patterns and oversee provisioning of compute clusters (AKS, AML Compute), networking, and storage for ML workloads.
Establish governance, drift monitoring, and enterprise grade model management practices.
Architect high availability, low latency inference environments using Azure Kubernetes Service, serverless compute (Azure Functions), and distributed microservice patterns.
Design and scale advanced RAG pipelines using vector databases such as Azure Cognitive Search, Redis, or FAISS, integrated with frameworks like Microsoft Agent Framework.
Set standards for Azure security (RBAC, identity, audit), logging, observability, and compliance across ML/AI environments.
Lead the creation of reusable, enterprise-wide Azure DevOps CI/CD templates for ML assets, ensuring consistency across data science teams.
Champion automated testing, gating, rollback strategies, quality controls, and monitoring for ML service deployments.
Establish platform wide SRE practices for ML systems, including reliability, scalability, and cost optimization frameworks.
Partner with Data Science, Cloud Architecture, and Product teams to deliver highly impactful AI solutions that scale globally.
Provide architectural guidance for real-time AI, conversational AI, enterprise search, analytics, and summarization workloads powered by LLMs.
Mentor ML engineers and advocate for engineering excellence, design reviews, and knowledge-sharing across teams.
Influence JCI’s AI platform roadmap, standards, and long-term architectural strategy.