About the role

Advanced AI Engineer at Honeywell designing AI algorithms and deploying solutions with a focus on Generative AI systems.

Responsibilities

Design, develop, and optimize Generative AI and Agentic AI solutions for 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.

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
6+ years of hands-on experience in AI/ML development, deployment, and productionization.
Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, and Scikit-learn.
Proven hands-on experience building LLM-based applications and AI agents using LangChain, LangGraph, or similar frameworks.
Experience deploying AI solutions on Azure, AWS, or GCP, with a solid understanding of cloud-native architectures.
Strong foundation in data structures, algorithms, and software engineering best practices.
Experience implementing MLOps pipelines, including CI/CD, model versioning, and monitoring.
Strong knowledge of Generative AI models, including Large Language Models (LLMs) and diffusion-based models.
Expertise in prompt engineering, retrieval-augmented generation (RAG), and tool-augmented LLM workflows.
Experience designing Agentic AI architectures, autonomous workflows, and multi-agent systems.
Hands-on experience with Databricks Mosaic AI, MLflow, and Unity Catalog for governed AI development.
Familiarity with CI/CD pipelines for AI/ML solutions and infrastructure-as-code practices.
Strong problem-solving skills with the ability to design scalable and maintainable AI systems.
Excellent communication skills, with the ability to explain complex AI concepts to both technical and non-technical stakeholders.