Senior Director of Software Engineering – Ai/ml & Ontology

Honeywell Honeywell · Industrial · Atlanta, GA +1

Senior Director of Software Engineering leading AI/ML and Ontology platforms, focusing on architecture, strategy, and execution of scalable, explainable AI-enabled solutions in an enterprise context. Requires deep expertise in AI/ML, ontology design, and leading senior technical teams.

What you'd actually do

  1. Define and own the end-to-end software architecture for AI/ML-enabled platforms, emphasizing ontologies, semantic models, and knowledge representation.
  2. Lead the design and implementation of scalable, production-grade AI/ML systems, including data pipelines, model lifecycle management, and inference services.
  3. Drive the development and adoption of enterprise ontologies and domain models to enable interoperability, reasoning, explainability, and data reuse across platforms.
  4. Partner with product, data science, and business leaders to identify high-value AI/ML use cases and translate them into executable engineering roadmaps.
  5. Lead and develop a small, elite team of senior architects, fostering a culture of technical excellence, accountability, and continuous learning.

Skills

Required

  • AI/ML expertise
  • Ontology design
  • Semantic modeling
  • Knowledge representation
  • Production-grade AI/ML systems
  • Scalable systems
  • Cloud-native architectures
  • Distributed systems
  • Software engineering best practices
  • Leadership of senior technical talent
  • Influencing organizational boundaries

Nice to have

  • PhD or advanced research background in AI, ML, or knowledge representation
  • MLOps platforms
  • Model governance
  • AI lifecycle management
  • Explainable AI (XAI)
  • Ethical AI
  • Regulatory considerations in enterprise environments
  • Industrial, enterprise, or highly regulated domains

What the JD emphasized

  • AI/ML-enabled platforms
  • ontologies, semantic models, and knowledge representation
  • scalable, production-grade AI/ML systems
  • enterprise ontologies and domain models
  • model governance, explainable AI (XAI), data quality, security, and compliance
  • high-value AI/ML use cases
  • model selection, training strategies, feature engineering, and MLOps
  • AI/ML, or knowledge representation
  • AI/ML-powered production systems at scale
  • ontology design, semantic modeling, knowledge graphs
  • MLOps platforms, model governance, and AI lifecycle management
  • explainable AI, ethical AI
  • responsible AI deployment

Other signals

  • design and implementation of scalable, production-grade AI/ML systems
  • enterprise ontologies and domain models
  • MLOps platforms, model governance, and AI lifecycle management