Sr. Director of Cloud Engineering

Johnson Controls•Westford, MA

2d•$170,000 - $240,000•Remote

About The Position

The Sr. Director of Cloud Engineering will lead cloud engineering and enterprise architecture across Johnson Controls’ Security organization. The role combines technical expertise with organizational leadership, overseeing engineering teams and delivery of cloud platforms, shared services, and AI-enabled capabilities that support multiple product lines. The position defines cloud and AI technical strategy, establishes architecture standards, and turns business priorities into executable engineering plans. The leader works with product-line engineering leaders and Product Management to align shared technology investments with product roadmaps, resolve cross-team dependencies, and deliver secure, scalable, and reliable solutions. The role requires balancing strategic decisions with detailed technical problem-solving while building strong teams and maintaining accountability for delivery, quality, and engineering effectiveness. This is a fully remote, U.S.-based position.

Requirements

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.
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 and 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.

Nice To Haves

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 management.
Success modernizing established products and introducing shared capabilities across a product portfolio.
Experience collaborating with globally distributed engineering teams.
Relevant cloud architecture certification.

Responsibilities

Define and execute cloud engineering and enterprise architecture strategy across Security, aligning investments with business priorities and product roadmaps.
Establish shared technical direction for cloud services, AI integration, data, APIs, identity, and connectivity among devices, on-premises systems, and cloud platforms.
Identify opportunities to consolidate capabilities, reduce duplication, and accelerate delivery through reusable platforms and services.
Partner with product-line leaders to establish ownership, prioritize shared investments, and balance immediate delivery with long-term modernization.
Evaluate emerging technologies and guide investment decisions based on customer value, feasibility, cybersecurity, and total cost of ownership.
Lead distributed engineering teams responsible for global cloud platforms and shared capabilities across product lines.
Establish responsibilities, decision authority, and collaboration practices across engineering managers, architects, and technical teams.
Recruit, develop, and retain engineering talent; strengthen leadership capability and succession readiness.
Manage staffing, capacity planning, engineering budgets, and external resources within the role’s scope.
Build a culture of customer focus, technical excellence, accountability, and continuous improvement.
Own delivery, quality, and technical outcomes for cloud and shared engineering capabilities.
Translate strategic priorities into plans with clear scope, milestones, resource commitments, and acceptance criteria.
Coordinate releases, manage dependencies, and resolve execution risks with Program Management and product-line engineering leaders.
Establish measures for delivery predictability, release quality, service reliability, engineering productivity, and operating cost.
Improve development, testing, deployment, and release practices through automation, CI/CD, and AI-assisted engineering tools.
Own architecture standards and governance across Security for cloud, hybrid, and connected security solutions.
Guide design decisions involving scalability, availability, interoperability, tenant isolation, performance, and recovery.
Lead architecture reviews and ensure significant technical decisions document 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 shared capabilities meet product and customer requirements.
Lead architecture and engineering integration of AI-enabled capabilities across products and shared services, in partnership with AI specialists and Product Management.
Establish services, data flows, interfaces, and deployment patterns for 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 experimentation into supported production capabilities.
Embed cybersecurity, observability, automated deployment, and recovery practices into cloud solutions.
Partner with Cybersecurity, DevOps, and product teams to address security, privacy, and compliance requirements.
Ensure operational readiness, 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.