Sr. Director Data & AI Platform Architect

Senior Director Data & Artificial Intelligence (AI) Platforms

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 out of our Atlanta, GA location on a hybrid work schedule.

• Platform Architecture Definition – define and evolve the enterprise Data & AI platform architecture spanning data, AI, and agent capabilities; establish standards for lakehouse, streaming, vector databases, MLOps, and real‑time inference platforms; 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.
• Emerging Technology Leadership – evaluate emerging AI, data, agentic, and cloud technologies and translate insights into platform strategy; lead proofs of concept and technology assessments to guide architecture and investment decisions; develop roadmap recommendations that balance innovation, business value, and operational readiness; build relationships with strategic technology partners, hyperscalers, and industry communities.
• Cloud, Edge & Hybrid Platforms – define scalable architecture patterns for cloud, on‑premises, edge, and hybrid AI deployments; establish solutions that address reliability, security, latency, data residency, and cost optimization; 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.
• Solution Architecture Community & Strategy – lead the Forge Data & AI Architecture community and establish enterprise standards and best practices; chair architecture reviews and governance forums to ensure alignment and consistency across initiatives; develop and maintain reference architectures, blueprints, design patterns, and architecture decision records (ADRs); partner with business, product, and engineering leaders to align platform investments with strategic priorities;
  
  mentor architects and drive adoption of scalable, reusable AI platform capabilities across the enterprise.

• 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 major hyperscaler, such as AWS, Azure, or GCP.
• Proven ability to architect end‑to‑end ML systems, including data 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.

• MS or PhD in Computer Science, Machine Learning, Data Engineering, or a related field, or equivalent applied experience.
• Experience designing hybrid or edge architectures in industrial or operational technology environments.
• Strong foundation in modern data architecture, including lakehouse, 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 with historian data, 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…