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As Senior Director Data & Artificial Intelligence (AI) Platform Architect at Honeywell Technologies, you will be responsible for defining and scaling the architecture for enterprise AI across cloud, edge, and hybrid environments. This leader will simplify complex platform landscapes, create reusable patterns, and align engineering, architecture, and business stakeholders around a practical strategy for Honeywell Forge AI growth. 

You will report directly to the Sr. Director Data & AI and work from our Atlanta, GA location on a hybrid work schedule.

Honeywell helps organizations solve the world's most complex challenges in automation, the future of aviation and energy transition. As a trusted partner, we provide actionable solutions and innovation through our Aerospace Technologies, Building Automation, Energy and Sustainability Solutions, and Industrial Automation business segments - powered by our Honeywell Forge software - that help make the world smarter, safer and more sustainable.

YOU MUST HAVE

• 10+ years of hands-on architecture experience designing production AI/ML or data platforms at enterprise scale.
• Deep experience with cloud AI and data services on at least one majorhyperscaler, such as AWS, Azure, or GCP.
• Proven ability toarchitectend-to-end ML systems, includingdata pipelines, feature engineering, training, serving, monitoring, feedback loops, and governance.
• Hands-on experience with LLM and agentic systems, including RAG, vector databases, orchestration frameworks, and inference optimization.

US PERSON REQUIREMENTS

Due to compliance with U.S. export control laws and regulations, candidate must be a U.S. Person, which is defined as, a U.S. citizen, a U.S. permanent resident, or have protected status in the U.S. under asylum or refugee status or have the ability to obtain an export authorization.

WE VALUE

• MS or PhD in Computer Science, Machine Learning, Data Engineering, orrelatedfield, or equivalent applied experience.
• Experience designing hybrid or edge architectures in industrial or operational technology environments.
• Strong foundationin modern data architecture, includinglakehouse, streaming, data governance, data quality, and data mesh concepts.
• Demonstrated success simplifying platforms, improving developer experience, reducing tool sprawl, and creating reusable architecture patterns.
• Industrial AI experience in areas such as predictive maintenance, quality inspection, process optimization, digital twins, supply chain, or energy management.
• Experience withhistoriandata, SCADA,IIoT, or industrial edge platforms.
• Knowledge of AI security and governance patterns, including responsible AI, audit logging, explainability, confidential computing, federated learning, or regulatory compliance.
• Experience with real-time or streaming AI systems, including low-latency feature computation, online learning, event-driven pipelines, or streaming inference.
• Multi-cloud or cloud-agnostic platform design experience using Kubernetes,KServe, Ray, Terraform, or similar abstraction layers.
• Open-source contributions, published architecture work, conference speaking, or recognized thought leadership in AI, data, or platform engineering.
• Strong executive communication skills and experience influencing senior technical and non-technical stakeholders.

BENEFITS OF WORKING FOR HONEYWELL TECHNOLOGIES

In addition to a competitive salary, leading-edge work, and developing solutions side-by-side with dedicated experts in their fields, Honeywell Technologies employees are eligible for a comprehensive benefits package. This package includes employer subsidized Medical, Dental, Vision, and Life Insurance; Short-Term and Long-Term Disability; 401(k) match, Flexible Spending Accounts, Health Savings Accounts, EAP, and Educational Assistance; Parental Leave, Paid Time Off (for vacation, personal business, sick time, and parental leave), and 12 Paid Holidays. For more information:Click Here

Honeywell is an equal opportunity employer. Qualified applicants will be considered without regard to age, race, creed, color, national origin, ancestry, marital status, affectional or sexual orientation, gender identity or expression, disability, nationality, sex, religion, or veteran status. Learn more about inclusion and engagement: Click Here

KEY RESPONSIBILITIES

PlatformArchitectureDefinition

• Define,architect,evolve,and executethe enterprise AI platform architecture spanning data, AI, and agent capabilities,from reference design to production implementation.
• Establish standards forlakehouse, streaming, vector databases,MLOps, and real-time inference platforms,and personallyvalidatethem through hands-on prototyping.
• Create reusable architecture patterns, governance guardrails, and golden-path templates that accelerate delivery.
• Simplify the technology landscape by reducing complexity, improving developer experience, and increasing platform scalability.

EmergingTechnology Leadership

• Evaluate emerging AI, data, agentic, and cloud technologies and translate insights into platformarchitecturedecisions.
• Leadhands-onproofs of concept and technology assessmentsthatinform build-vs-buyand investment decisions.
• Develop roadmap recommendations that balance innovation, business value, and operational readiness.

Cloud, Edge & Hybrid Platforms

• Define scalable architecture patterns for cloud, on-premises, edge, and hybrid AI deployments.
• Designforreliability, security, latency, data residency, and cost optimizationacross training and inferenceworkloads.
• Lead architecture for industrial edge AI, including real-time inference and OT/IT integration.
• Drive resilient and resilient-by-design platform capabilities across training, inference, and data workloads.

SolutionArchitecture Community& Strategy

• Lead the Forge Data & AI Architecture community andestablishenterprise standards and best practices.
• Chair architecture reviews to ensure alignment and consistencywithplatform standards.
• Maintain reference architectures, blueprints, design patterns, and architecture decision records (ADRs) as the platform's technical source oftruth.
• Partner with business, product, and engineering leaders to align platform investments with strategic priorities.
• Mentor architects and drive adoption of scalable, reusable AI platformpatternsyou define.