This role operates as the enterprise service owner for AI and data-driven applications, ensuring platforms are secure, reliable, scalable, compliant, and production-ready. The Director partners closely with business leaders, data science teams, platform engineering, cybersecurity, and global IT stakeholders to ensure AI solutions are industrialized and adopted without business disruption, while enabling innovation and transformation across Aerospace.

Experience & Capabilities:

· 15+ years of experience in enterprise IT leadership, platform ownership, or large-scale technology delivery roles

· Proven experience acting as service owner for complex, business-critical platforms

· Strong background in AI platforms, data ecosystems, and cloud-based architectures

· Demonstrated success leading enterprise transformations and technical implementations

· Experience operating in global, regulated, mission-critical environments

Education:

· Bachelor’s or Master’s degree in Engineering, Computer Science, Information Technology, or a related field

Who Will Succeed in This Role:

· Seasoned technology transformation leaders with strong service-ownership mindset

· Leaders who can plan, design, build, and support large-scale enterprise platforms

· Professionals who balance innovation with operational excellence

· Executives capable of driving AI adoption and business transformation without disrupting operations

Key Responsibilities:

AI Platform & Application Service Ownership:

· Own end-to-end service accountability for AI applications and platforms, including:

o AI application lifecycle management

o Platform availability, reliability, and performance

o MLOps, model lifecycle, and operational readiness

· Ensure AI platforms support enterprise-scale development, deployment, monitoring, and retraining of models

· Act as the single point of accountability for AI service health, uptime, and support across Aerospace

AI & MLOps Enablement:

· Own and govern MLOps capabilities across AI platforms, including:

o Model deployment and versioning

o Monitoring, drift detection, and retraining workflows

o Secure model serving and access controls

· Partner with data science and AI engineering teams to industrialize AI solutions and move them from experimentation to production

Technology Leadership & Transformation:

· Serve as a technology transformation leader, driving adoption of AI platforms as strategic business enablers

· Identify and sponsor innovation initiatives that accelerate productivity, speed, and business outcomes using AI

· Ensure AI platforms align with Honeywell’s responsible AI, security, and governance standards

1. Strategy, Roadmap & AOP Ownership

· Partner with business stakeholders to define AI platform and application strategy, multi-year roadmaps, and AOP plans

· Translate business demand into prioritized, funded AI initiatives

· Ensure traceability from strategy → roadmap → AOP → delivery → adoption

Cross-Functional Leadership & Enterprise Integration:

· Work with enterprise architecture, data, cloud, cybersecurity, and platform teams to deliver integrated AI solutions

· Enable end-to-end solution support across data ingestion, model development, application layers, and enterprise systems

· Drive alignment across global and regional teams to ensure consistency with Honeywell’s enterprise operating model

1. Delivery, Investment & Operational Excellence

· Own delivery governance, investment prioritization, and execution oversight for AI platforms and applications

· Ensure solutions are delivered and supported without business interruptions

· Balance innovation speed with stability, compliance, and operational rigor

1. People, Team & Platform Leadership

· Lead and develop managers, architects, AI engineers, and platform teams

· Build high-performing teams responsible for planning, designing, building, and supporting AI platforms and applications

· Foster a culture of ownership, accountability, and continuous improvement

Technology & Platform Landscape (Honeywell-Aligned)

· AI & Data Platforms

o Databricks – Enterprise AI/ML and analytics platform

o Snowflake – Enterprise Data Warehouse supporting AI consumption

· Cloud Platforms

o Google Cloud Platform (GCP) – Engineering and AI data platforms

o Microsoft Azure – AI integration, Azure OpenAI, orchestration

o AWS – Select enterprise AI and analytics services

· AI & MLOps

o Model serving, monitoring, CI/CD for ML

o Vector databases and RAG architectures supporting AI applications

· Security & Identity

o Enterprise IAM, RBAC, SSO, and secure access patterns

· Enterprise Integration

o APIs, event-driven architectures, and integration with core enterprise systems