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.
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 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.
