Honeywell

2 months ago

Advanced AI Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 11 hours agoStatus: Live

Job Summary

Design, develop, and deploy enterprise-grade Generative and Agentic AI solutions. Build AI-powered agents and multi-agent systems, architect end-to-end AI/ML pipelines, and deploy on cloud with Docker/Kubernetes. Ensure scalability, security, observability, and responsible AI, leveraging LangChain/LangGraph, Databricks Mosaic AI, MLflow, and modern MLOps practices.

Location: Charlotte, Atlanta

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key 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.

Key Requirements

•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.

Experience:6+ yearsGenerative AILLMsAI agentsCloud computingMLOps

Education:Bachelor's

Skills:PythonPyTorchTensorFlowScikit-learnLangChainLangGraphDatabricks Mosaic AIMLflowDockerKubernetesUnity CatalogAzureAWSGCPCI/CDObservability

Languages:English

Tech Stack:PythonPyTorchTensorFlowScikit-learnLangChainLangGraphDatabricks Mosaic AIMLflowDockerKubernetesUnity CatalogAzureAWSGCPCI/CDObservability

Company Brief

Honeywell

Global diversified technology and manufacturing company providing aerospace systems, building technologies, performance materials, and safety & productivity solutions for industrial, commercial, and consumer markets.

Industry: Conglomerates & Holding Companies

Company Size: Enterprise (1,001+ employees)

Revenue: USD 25B to 50B

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Charlotte, United States

Founded: 1906

Glassdoor: 3.8

Website LinkedIn Glassdoor

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Honeywell

2 months ago

Advanced AI Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 11 hours agoStatus: Live

Job Summary

Location: Charlotte, Atlanta

Workplace: Hybrid

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key 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.

Key Requirements

•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.

Experience:6+ yearsGenerative AILLMsAI agentsCloud computingMLOps

Education:Bachelor's

Skills:PythonPyTorchTensorFlowScikit-learnLangChainLangGraphDatabricks Mosaic AIMLflowDockerKubernetesUnity CatalogAzureAWSGCPCI/CDObservability

Languages:English

Tech Stack:PythonPyTorchTensorFlowScikit-learnLangChainLangGraphDatabricks Mosaic AIMLflowDockerKubernetesUnity CatalogAzureAWSGCPCI/CDObservability

Company Brief

Honeywell

Industry: Conglomerates & Holding Companies

Company Size: Enterprise (1,001+ employees)

Revenue: USD 25B to 50B

Growth: Public Company

Valuation: Public Company (Market Cap in USD)

Funding: IPO / Publicly Listed

Headquarters: Charlotte, United States

Founded: 1906

Glassdoor: 3.8

Website LinkedIn Glassdoor