What are the responsibilities and job description for the Advanced AI Engineer position at Honeywell?
As an Advanced AI Engineer, you will design, develop, and deploy cutting-edge AI solutions with a strong focus on Generative AI (GenAI) and Agentic AI systems. You will build intelligent, autonomous AI agents using modern orchestration frameworks such as LangChain, LangGraph, and Databricks Mosaic AI Agent Framework, delivering scalable, secure, and production-ready AI systems tightly integrated with enterprise workflows.
You will collaborate closely with cross-functional teams to identify high-impact AI use cases, architect end-to-end solutions, and operationalize AI at scale using cloud platforms and MLOps best practices.
You will report directly to our Sr. AI Manager and you’ll work out of our Charlotte, NC or Atlanta, GA locations on a Hybrid work schedule.
In this role, you will impact critical AI initiatives and drive technical excellence within the organization. This is a unique opportunity to play a key role in shaping the future of AI technology within a company known for its commitment to excellence. Let's shape the future together!
KEY RESPONSIBILITIES
- Design, develop, and optimize Generative AI and Agentic AI solutionsfor real-world, enterprise-grade applications.
- Build and orchestrate AI-powered agents and multi-agent systems using frameworks such as LangChain, LangGraph, and Databricks Mosaic AI Agent Framework.
- Architect and implement end-to-end AI pipelines, including data ingestion, feature engineering, model training, evaluation, and inference.
- Collaborate with product, data, platform, and business stakeholders to identify AI use cases and translate requirements into scalable AI solutions.
- Deploy and manage AI models and agents on cloud platforms (Azure, AWS, or GCP)using containerization (Docker/Kubernetes) and modern MLOps practices.
- Implement model monitoring, observability, and performance tracking to ensure accuracy, reliability, and responsible AI usage in production.
- Leverage MLflow for experiment tracking, model versioning, and lifecycle management.
- Utilize Databricks AI/ML Platform, including Unity Catalog, for governed data access, feature management, and secure AI deployments.
- Ensure AI systems meet enterprise standards for scalability, security, compliance, and maintainability.
- Stay current with emerging AI technologies, frameworks, and research, driving innovation and continuous improvement across AI solutions.