Advanced AI Engineer

Honeywell Honeywell · Industrial · Charlotte, NC +1

This role focuses on designing, developing, and deploying Generative AI and Agentic AI systems, including building AI agents using orchestration frameworks and operationalizing AI at scale on cloud platforms with MLOps best practices. The role involves architecting end-to-end AI pipelines, deploying models, and ensuring monitoring and observability.

What you'd actually do

  1. Design, develop, and optimize Generative AI and Agentic AI solutions for real-world, enterprise-grade applications.
  2. Build and orchestrate AI-powered agents and multi-agent systems using frameworks such as LangChain, LangGraph, and Databricks Mosaic AI Agent Framework.
  3. Architect and implement end-to-end AI pipelines, including data ingestion, feature engineering, model training, evaluation, and inference.
  4. Collaborate with product, data, platform, and business stakeholders to identify AI use cases and translate requirements into scalable AI solutions.
  5. Deploy and manage AI models and agents on cloud platforms (Azure, AWS, or GCP) using containerization (Docker/Kubernetes) and modern MLOps practices.

Skills

Required

  • Python
  • PyTorch
  • TensorFlow
  • Scikit-learn
  • LangChain
  • LangGraph
  • Azure
  • AWS
  • GCP
  • Docker
  • Kubernetes
  • MLOps pipelines
  • CI/CD
  • model versioning
  • monitoring

Nice to have

  • Generative AI models
  • Large Language Models (LLMs)
  • diffusion-based models
  • prompt engineering
  • retrieval-augmented generation (RAG)
  • tool-augmented LLM workflows
  • Agentic AI architectures
  • autonomous workflows
  • multi-agent systems
  • Databricks Mosaic AI
  • MLflow
  • Unity Catalog
  • CI/CD pipelines for AI/ML solutions
  • infrastructure-as-code practices

What the JD emphasized

  • 6+ years of hands-on experience in AI/ML development, deployment, and productionization.
  • 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
  • Strong knowledge of Generative AI models, including Large Language Models (LLMs) and diffusion-based models.
  • Experience designing Agentic AI architectures, autonomous workflows, and multi-agent systems.

Other signals

  • Generative AI
  • Agentic AI systems
  • LLM-based applications
  • end-to-end AI pipelines
  • MLOps best practices
  • cloud platforms
  • productionization