Sr. Manager, Platform Engineering

Johnson Controls•Glendale, AZ

19h•$100,000 - $140,000

About The Position

Johnson Controls is seeking a Manager of AI & ML Platform Engineering to lead their growing AI and Data Platform team. This role is responsible for defining the vision, strategy, standards, and delivery model for enterprise AI platforms that enable machine learning, generative AI, and agentic AI capabilities at scale across JCI. The successful candidate will lead a team of ML Platform Engineers and work at the intersection of AI, cloud engineering, platform operations, governance, and enterprise architecture. You will be responsible for building and operating the foundational capabilities that support model training, LLM inference, RAG architectures, AI agents, and enterprise AI adoption.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related discipline.
10+ years experience in software engineering, cloud engineering, platform engineering, or AI/ML technologies.
5+ years leading engineering teams in enterprise environments.
Strong experience delivering Azure-based cloud and AI platform solutions.
Experience operating large-scale platforms supporting AI, machine learning, or data products.
Expert knowledge of Azure, Azure ML, Azure OpenAI, Azure AI Foundry, Azure DevOps, Terraform, Kubernetes, and AKS.
Strong understanding of MLOps, LLMOps, platform engineering, CI/CD, Infrastructure as Code, and cloud security.
Experience with vector databases, RAG architectures, AI agents, and enterprise GenAI solutions.
Strategic thinker with a platform-first mindset.
Excellent communication, stakeholder management, and organisational leadership skills.
Strong problem-solving capability with a focus on business impact and operational excellence.

Nice To Haves

Experience building enterprise GenAI and agentic AI platforms.
Knowledge of Responsible AI, governance frameworks, privacy, and compliance requirements.
Azure certifications such as Azure Solutions Architect, Azure AI Engineer, or Terraform Associate.

Responsibilities

Define and execute the enterprise AI Platform roadmap aligned to business and technology objectives.
Establish standards, reference architectures, engineering patterns, and operating procedures for AI and ML workloads.
Drive adoption of AI platform capabilities across product teams, business units, and data science organizations.
Own platform KPIs including reliability, security, adoption, developer productivity, and cost efficiency.
Lead the design and operation of enterprise AI platforms on Azure including Azure AI Foundry, Azure OpenAI, Azure ML, AKS, Azure Functions, and App Services.
Oversee scalable environments for model training, evaluation, deployment, monitoring, and inference.
Define platform standards for RAG solutions, vector databases, LLM serving, agent orchestration, and AI application integration.
Ensure security, observability, RBAC, auditability, and compliance controls are consistently implemented across environments.
Own platform operations including incident management, service reliability, escalation processes, root cause analysis, and post-incident improvement actions.
Establish enterprise standards for model lifecycle management, feature management, model monitoring, drift detection, and AI governance.
Drive Infrastructure as Code adoption using Terraform and standardised Azure DevOps delivery pipelines.
Implement governance processes covering platform architecture, technology standards, change management, and Responsible AI practices.
Lead, mentor, and develop a team of ML Platform Engineers, MLOps Engineers, and cloud specialists.
Build a high-performing engineering culture focused on automation, delivery excellence, innovation, and continuous improvement.
Support recruitment, career development, succession planning, and technical leadership development.
Partner with Data Science, Product Engineering, Cyber Security, Enterprise Architecture, and Digital Technology leadership teams.
Provide technical and strategic guidance on AI platform investments, scalability, governance, and operational excellence.
Communicate platform strategy, risks, and progress to senior leadership and executive stakeholders.