Director AI & Ontology Engineering for Scalable Platforms

Honeywell Technologies; Atlanta (GA); Hybrid; USD 180,000 - 300,000; Full time

Benefits offered by this job

Medical Insurance

Dental Insurance

Vision Insurance

401(k) match

Job summary

Honeywell Technologies is seeking a Senior Director of Software Engineering - AI & Ontology to shape architecture for next-generation AI platforms in a hybrid Atlanta, GA setting. You will mentor senior architects and influence executives while delivering scalable, explainable AI-enabled solutions.

You will partner with product, data science, and business leaders to identify high-impact AI/ML use cases and translate them into engineering roadmaps, guiding model selection, training, and MLOps

Qualifications

• 10+ years of progressive software engineering experience.
• Hands-on ontology design or semantic modeling experience.
• Proven track record delivering AI/ML-powered production systems at scale.
• Combination of technical depth and strategic leadership.

Responsibilities

• Define and own the end-to-end software architecture for AI/ML-enabled platforms, emphasizing ontologies and knowledge representation.
• Lead scalable, production-grade AI/ML systems including data pipelines and model lifecycle management.
• Drive enterprise ontologies and domain models for interoperability, reasoning, and data reuse.
• Ensure alignment across cloud, edge, and on-prem deployments; govern model governance and XAI.

Skills

Software architecture leadership

Cloud-native architectures

Ontology design

AI/ML production systems

Mentoring senior architects

Education

Bachelor's or higher in CS/Engineering/AI

Tools

MLOps platforms

Job description

Honeywell Technologies is seeking a Senior Director of Software Engineering - AI & Ontology to shape architecture for next-generation AI platforms in a hybrid Atlanta, GA setting. You will mentor senior architects and influence executives while delivering scalable, explainable AI-enabled solutions.

You will partner with product, data science, and business leaders to identify high-impact AI/ML use cases and translate them into engineering roadmaps, guiding model selection, training, and MLOps