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JCI

Sr. Director of Cloud Engineering

Westford-Massachusetts-United States of America

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hirly's read of this role

Seniority
Director
Country
US
Work mode
On-site / unstated
First seen by hirly
5 Nov 2026

Derived automatically from the posting. Upload your resume above to see how the role scores against it.

the posting

About Johnson Controls

Johnson Controls develops technologies that help customers create safer, smarter, and more connected environments. Our Security Solutions portfolio brings together access control, video management, cameras, and cloud services to help customers protect people, facilities, and assets.

As we advance our cloud and AI capabilities, we are seeking a senior engineering leader to shape the technical strategy and foundations of our next-generation security solutions, including Highspan and the evolution of our established product portfolio.

Your Role

The Sr. Director of Cloud Engineering will lead cloud engineering and enterprise architecture across Johnson Controls’ Security organization. This role combines deep technical expertise with organizational leadership, managing engineering teams and owning delivery of cloud platforms, shared services, and AI-enabled capabilities that support multiple product lines.

You will define the cloud and AI technical strategy, establish architecture standards, and translate business priorities into executable engineering plans. Working with product-line engineering leaders and Product Management, you will align shared technology investments with product roadmaps, resolve cross-team dependencies, and deliver secure, scalable, and reliable solutions.

This position requires a leader who can move between strategic decisions and detailed technical problem-solving, build strong teams, and maintain accountability for delivery, quality, and engineering effectiveness.

This is a fully remote U.S.-based position

Your Responsibilities

Strategic Leadership

Define and execute the cloud engineering and enterprise architecture strategy across Security, aligning technology investments with business priorities and product roadmaps.

Establish a shared technical direction spanning cloud services, AI integration, data, APIs, identity, and connectivity between devices, on-premises systems, and cloud platforms.

Identify opportunities to consolidate overlapping capabilities, reduce duplication, and accelerate delivery through reusable platforms and services.

Partner with product-line leaders to establish clear ownership, prioritize shared engineering investments, and balance immediate delivery needs with long-term modernization.

Evaluate emerging technologies and guide investment decisions based on customer value, technical feasibility, cybersecurity, and total cost of ownership.

Engineering Organization Leadership

Lead distributed engineering teams responsible for cloud platforms globally and shared capabilities serving multiple product lines.

Establish clear responsibilities, decision authority, and collaboration practices across engineering managers, architects, and technical teams.

Recruit, develop, and retain engineering talent, strengthen leadership capability, and build succession readiness.

Manage staffing, capacity planning, engineering budgets, and external resources for the functions within the role’s scope.

Build a culture of customer focus, technical excellence, accountability, and continuous improvement.

Delivery and Engineering Execution

Own delivery, quality, and technical outcomes for the cloud and shared engineering capabilities within the organization’s remit.

Translate strategic priorities into achievable plans with clear scope, milestones, resource commitments, and acceptance criteria.

Partner with Program Management and product-line engineering leaders to coordinate releases, manage dependencies, and resolve execution risks.

Establish measures of delivery predictability, release quality, service reliability, engineering productivity, and operating cost.

Drive improvements in development, testing, deployment, and release practices through automation, CI/CD, and effective use of AI-assisted engineering tools.

Enterprise Architecture and Technical Leadership

Own architecture standards and governance across Security, providing practical direction for cloud, hybrid, and connected security solutions.

Guide critical design decisions involving scalability, availability, interoperability, tenant isolation, performance, and recovery.

Lead architecture reviews and ensure significant technical decisions include clear alternatives, trade-offs, and implementation guidance.

Maintain hands-on technical involvement through design reviews, targeted prototypes, and resolution of complex engineering issues.

Work with product-line teams to implement architecture improvements and verify that shared capabilities meet product and customer requirements.

AI Architecture and Integration

Lead the architecture and engineering integration of AI-enabled capabilities across products and shared services, in partnership with AI specialists and Product Management.

Establish the services, data flows, interfaces, and deployment patterns needed to operate AI capabilities across cloud and edge environments.

Define evaluation and monitoring approaches covering accuracy, latency, reliability, cost, and customer value.

Embed data privacy, access controls, model security, and appropriate human oversight into AI-enabled solutions.

Guide emerging AI technologies from focused experimentation into supported production capabilities.

Security and Operational Excellence

Establish engineering practices that embed cybersecurity, observability, automated deployment, and recovery into cloud solutions.

Partner with Cybersecurity, DevOps, and product teams to address applicable security, privacy, and compliance requirements.

Ensure operational readiness of delivered services, including monitoring, support ownership, incident response, and disaster recovery planning.

Manage cloud and AI cost efficiency through architecture choices, capacity planning, and resource optimization.

Use production feedback and engineering metrics to improve reliability, performance, and cost to serve.

What We Are Looking For

Basic Qualifications

Bachelor’s degree in Computer Science, Software Engineering, Computer Engineering, or a related technical discipline, or equivalent relevant experience.

12+ years of software engineering experience, including substantial experience designing and delivering cloud or distributed systems and 5+ years leading engineering teams.

Demonstrated experience leading engineering managers, architects, and globally distributed technical teams.

Proven accountability for delivery, quality, staffing, and engineering budgets for commercial software platforms or shared services.

Experience leading technical strategy and coordinating delivery across multiple product lines or business units.

Demonstrated experience architecting production SaaS platforms, including multi-tenancy, service integration, data management, and operational resilience.

Deep expertise in a major public cloud platform, with the ability to apply sound architecture principles across different technology environments.

Practical experience integrating AI or machine learning capabilities into production software, including deployment, evaluation, and monitoring.

Strong understanding of APIs, event-driven systems, containers, infrastructure automation, and CI/CD.

Experience designing secure systems, including identity, authorization, encryption, and customer data protection.

Ability to produce working prototypes, assess implementation details, and influence technical decisions across multiple teams.

Preferred Qualifications

Advanced degree (MS, MBA, or PhD) in a relevant technical or business discipline.

Experience with Microsoft Azure.

Background in physical security, video management, access control, IoT, or connected-device platforms.

Experience with video processing, streaming, storage, or edge AI deployment.

Experience with generative AI applications, retrieval-based systems, model-serving infrastructure, or AI lifecycle manage

Original posting on JCI's site ↗

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