Sr. Director Data & AI Platforms

We are seeking a Senior Director of Forge Data, AI and Agent Platform wwho thrives at the intersection of deep platform engineering and forward-looking architecture strategy — a technologist who can design the systems that power AI at industrial scale today while anticipating what the next generation of AI-native platforms will demand tomorrow.

You will define how data, AI models, and autonomous agents are architected across cloud, on-premises, and hybrid edge environments. You will simplify complexity — turning a sprawling landscape of tools and capabilities into coherent, operable, and evolvable platforms. And you will be the connective force that brings together solution architects, engineering leaders, and business stakeholders into a unified strategy for growth of Forge AI for Honeywell Automation portfolio. The Senior Director will be both strategic and hands-on, setting technical direction while mentoring senior architects and influencing executive stakeholders.

Key Responsibilities

• Own and evolve the canonical reference architecture for the Industrial AI platform — spanning data ingestion, processing, model serving, and agentic orchestration layers.
• Define the architecture of the enterprise AI data platform including lakehouse, feature stores, vector databases, streaming pipelines, and real-time inference infrastructure.
• Architect the agent platform: design the orchestration frameworks, tool registries, memory systems, and safety guardrails that enable reliable multi-agent AI workflows at enterprise scale.
• Establish platform layering principles — separating concerns between infrastructure, platform services, AI capabilities, and application-level solutions to ensure modularity and replaceability.
• Drive platform simplification initiatives: consolidate redundant tooling, reduce operational surface area, and establish "golden path" patterns that make building AI applications faster and more reliable.

• Maintain a continuous technology watch across AI platform, data engineering, agent frameworks, and edge computing domains — synthesizing signals from research, open-source, and vendor communities into actionable architectural guidance.
• Lead structured evaluation of emerging technologies (new foundation model APIs, agentic frameworks, vector retrieval architectures, edge AI runtimes, next-gen data formats) using rigorous PoC and architecture fitness criteria.
• Serve as the organization's internal thought leader on platform evolution — publishing architecture decision records, technology briefings, and roadmap recommendations to CoE and enterprise leadership.
• Build relationships with hyperscaler architecture teams, AI platform vendors, and open-source project leads to gain early visibility into emerging capabilities and influence platform direction.
• Identify and mitigate architectural technical debt proactively, proposing migration paths before legacy patterns constrain AI capability delivery.

• Design cloud-native AI platform architectures on major hyperscalers including managed AI/ML services, serverless inference, cloud-native data platforms, and AI gateway patterns.
• Architect for edge and near-edge AI deployment patterns for industrial environments: model compression and optimization for edge hardware, OT/IT integration, edge inference orchestration, and edge-to-cloud data synchronization.
• Define hybrid architecture patterns that span cloud and on-premises — addressing data residency requirements, network latency constraints, air-gapped environments, and operational consistency across deployment tiers.
• Design for industrial-grade reliability: architect patterns for fault tolerance, graceful degradation, offline operation, and deterministic failover in environments where downtime has direct operational consequences.
• Establish FinOps-aligned architecture patterns that balance AI platform capability with cloud cost optimization across training, inference, and data processing workloads.

• Convene and lead the Forge Data and AI Architecture Forum across the enterprise with various product architecture teams and align on standards and changes.
• Define and govern architecture review processes for Data and AI initiatives: establish design review cr