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 organizations 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 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 theForge 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 DataAI 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 organizations 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.