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 Platform Architecture Definition 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. Emerging Technology Leadership 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. Cloud, Edge & Hybrid Architecture 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. Solution Architecture Community & Strategy 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 criteria, facilitate reviews, document decisions, and maintain an architecture decision record (ADR) library. Partner with solution architects embedded in business domains to translate domain‑specific AI requirements into platform capability investments and reusable architecture patterns. Drive consistency across the architect community by developing shared pattern libraries, reference implementations, and architecture blueprints that accelerate solution design across the enterprise. Represent the Forge AI architecture perspective in enterprise architecture governance bodies, ensuring AI requirements are reflected in enterprise technology standards and roadmaps. Required Qualifications AI & ML Platform Architecture : 10+ years of hands‑on architecture experience designing production AI/ML platforms. Demonstrated ability to architect end‑to‑end ML systems: data pipelines, feature engineering, model training, serving, monitoring, and feedback loops at enterprise scale. Cloud Data & AI Services Expertise : Deep, production‑proven expertise with cloud AI and data services on at least one major hyperscaler (AWS SageMaker / Bedrock, Azure ML / OpenAI Service / Fabric, or GCP Vertex AI / BigQuery). Ability to architect multi‑cloud or cloud‑agnostic AI platforms. Agentic AI & LLM Architecture : Hands‑on architecture experience with large language model platforms and agentic systems, including RAG pipeline design, tool‑use frameworks, multi‑agent orchestration patterns (LangGraphor equivalent), vector database selection and integration, and LLM inference optimization. Hybrid & Edge Architecture : Proven experience designing hybrid or edge deployment architectures — including at least one industrial or operational technology (OT) environment. Understanding of edge inference runtimes, OT/IT network segmentation, data sovereignty constraints, and real‑time latency requirements. Platform Simplification & Developer Experience : Track record of reducing platform complexity — consolidating toolchains, designing internal developer platforms, establishing golden‑path templates, and measurably improving developer productivity and system operability for AI teams. Architecture Leadership & Community Building : Experience leading architecture communities of practice, facilitating architecture review boards, and producing governance artifacts (ADRs, reference architectures, technology radars) that are actively adopted by engineering teams. Stakeholder Communication & Executive Influence : Demonstrated ability to present complex architectural strategies to executive and non‑technical audiences, build cross‑functional alignment, and influence technology investment decisions at senior levels. Data Architecture Foundations : Strong grounding in modern data architecture: Lakehouse (Delta Lake / Iceberg), streaming platforms (Kafka / Flink / Spark Streaming), data mesh principles, data governance integration, and data quality at scale. MLOps & AI Lifecycle Platforms : Deep experience with MLOps platforms (MLflow, Kubeflow, or cloud‑native equivalents), including automated retraining pipelines, model governance, drift detection, A/B testing infrastructure, and AI audit trail design. Preferred Qualifications MS or PhD in Computer Science, Machine Learning, Data Engineering, or a related field — or equivalent deep self‑directed research and applied experience in AI systems design. Industrial Domain Knowledge: Familiarity with industrial AI use cases: predictive maintenance, quality inspection, process optimization, supply chain AI, digital twins, or energy management. Experience integrating historian data (OSIsoft PI / AVEVA), SCADA, or IIoT platforms is a significant differentiator. Confidential Computing & AI Security: Knowledge of data security architectures for AI: confidential computing, differential privacy, federated learning, model watermarking, adversarial robustness patterns, and AI‑specific access control design. Open Source Contributions or Thought Leadership: Active contributions to open‑source AI or data projects, published architecture papers, conference presentations (NeurIPS, Data+AI Summit, KubeCon, re:Invent, etc.), or recognized industry blog authorship in AI platform domains. Real‑Time & Streaming AI Systems: Architecture experience with real‑time AI systems: low‑latency feature computation, online learning, streaming inference, event‑driven AI pipelines, and complex event processing in industrial or financial contexts. Multi‑Cloud & Cloud‑Agnostic Platform Design: Experience designing portable AI platforms using abstraction layers (Kubernetes, KServe, Ray, Terraform) that minimize hyperscaler lock‑in while leveraging cloud‑native capabilities where appropriate. AI Governance & Responsible AI Architecture: Knowledge of responsible AI architecture patterns: explainability infrastructure, bias detection pipelines, human‑in‑the‑loop systems, AI audit logging, regulatory compliance architectures (EU AI Act, ISO 42001). What Success Looks Like Forge AI Platform is successfully adopted across the enterprise, standardized architectures support Honeywell Forge product portfolio. Ability to experiment pre‑release frameworks, and form opinions about emerging technologies before they are mainstream. Distill signal vs. noise for right enterprise decision. Reduce complexity, find elegant solutions that are easier to build, operate, and evolve, and they resist the pull of unnecessary sophistication. Consensus through credibility, clear communication, and genuine partnership. Align senior architects around a shared direction. Industrial AI has operational constraints — reliability, safety, latency, security. Architect platform and design decisions need to adapt accordingly. Produce clear, durable ADRs, reference architectures, and design guides that are published the enterprise to use. The organization’s AI capabilities mature in a responsible, sustainable, and enterprise‑ready way.

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. 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. #J-18808-Ljbffr Honeywell International, Inc.