Hybrid Advanced AI Engineer

at Honeywell

• Job title: Advanced AI Engineer
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: AWS, Azure, Cloud, Docker, Google Cloud Platform, Kubernetes, Python, PyTorch, Scikit-Learn, Tensorflow, Unity
• Location requirements: Hybrid, Bengaluru, India

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.

Requirements

• 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.

Benefits

• Health insurance
• Retirement plans
• Paid time off
• Flexible work arrangements
• Professional development