The Role
Design, develop, evaluate, deploy, and optimize machine learning, deep learning, and generative AI solutions. Build LLM, RAG, chatbot, recommendation, predictive analytics, and AI agent applications; develop scalable data and MLOps pipelines; fine-tune foundation models; and implement monitoring, governance, explainability, privacy, and responsible AI practices. Collaborate with cross-functional teams to translate business requirements into efficient, production-ready AI solutions.
Summary Generated by Built In
Responsibilities
About UsHoneywell Technologies is a global, pure-play automation company with a legacy of innovating to help solve the world’s most mission-critical challenges, enhancing the quality of life for people and communities around the world. We serve the building, industrial and process sectors with a broad portfolio of services, solutions and products, underpinned by our Honeywell Technologies Accelerator operating system and Honeywell Technologies Forge intelligence layer. By combining the deep domain expertise of our more than 50,000 employees with decades of data from our global installed base, we are uniquely positioned to lead the industrial sector’s transition from automation to autonomy.
Key Responsibilities
- Design, develop, train, evaluate, and deploy machine learning and deep learning models.
- Develop and optimize Generative AI applications using Large Language Models (GPT, Llama, Claude, Mistral, etc.).
- Build Retrieval-Augmented Generation (RAG) solutions using vector databases.
- Fine-tune foundation models and implement prompt engineering strategies.
- Create scalable AI pipelines for data ingestion, feature engineering, model training, and inference.
- Develop AI-powered applications, chatbots, recommendation engines, and predictive analytics solutions.
- Implement MLOps practices including automated training, deployment, monitoring, and retraining.
- Work with cross-functional teams to translate business requirements into AI solutions.
- Optimize model performance, latency, accuracy, and cost efficiency.
- Ensure AI governance, model explainability, privacy, and responsible AI practices.
- Stay current with emerging AI technologies and recommend innovations.
Required Skills & Qualifications
Technical Skills
- Machine Learning & AI
- Generative AI
- Strong understanding of: Supervised and Unsupervised Learning, Regression and Classification, Clustering, Recommendation Systems, Time-Series Forecasting, Reinforcement Learning, Deep Learning
- Experience with: LLMs (GPT, Llama, Gemini, Claude, Mistral), RAG architectures, Prompt Engineering, Fine-tuning and PEFT techniques, LangChain / LangGraph, Semantic Kernel, AI Agents and Multi-Agent Systems
- Programming
- Expert-level Python development
- Strong knowledge of: NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch
- MLOps & Deployment: Docker, Kubernetes, MLflow, CI/CD pipelines, Model Monitoring, Azure ML / AWS SageMaker / Vertex AI
- Data Technologies: SQL, Spark/PySpark, Data Engineering concepts, ETL pipelines, Feature Stores
- Vector Databases: Pinecone, ChromaDB, Weaviate, Azure AI Search, FAISS
- Cloud Platforms
- Experience in at least one:
Microsoft Azure AI Services
Azure OpenAI
AWS AI/ML Services
Google Vertex AI
About UsHoneywell Technologies is a global, pure-play automation company with a legacy of innovating to help solve the world’s most mission-critical challenges, enhancing the quality of life for people and communities around the world. We serve the building, industrial and process sectors with a broad portfolio of services, solutions and products, underpinned by our Honeywell Technologies Accelerator operating system and Honeywell Technologies Forge intelligence layer. By combining the deep domain expertise of our more than 50,000 employees with decades of data from our global installed base, we are uniquely positioned to lead the industrial sector’s transition from automation to autonomy.
Skills Required
- Expert-level Python development
- Strong understanding of supervised and unsupervised learning, regression, classification, clustering, recommendation systems, time-series forecasting, reinforcement learning, and deep learning
- Experience with large language models including GPT, Llama, Gemini, Claude, or Mistral
- Experience with RAG architectures, prompt engineering, fine-tuning, and PEFT techniques
- Experience with LangChain, LangGraph, Semantic Kernel, AI agents, and multi-agent systems
- Strong knowledge of NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch
- Experience with Docker, Kubernetes, MLflow, CI/CD pipelines, model monitoring, and cloud ML platforms
- Experience with Azure ML, AWS SageMaker, or Vertex AI
- Knowledge of SQL, Spark or PySpark, data engineering concepts, ETL pipelines, and feature stores
- Experience with vector databases including Pinecone, ChromaDB, Weaviate, Azure AI Search, or FAISS
- Experience with at least one cloud AI platform: Microsoft Azure AI Services, Azure OpenAI, AWS AI/ML Services, or Google Vertex AI
- Ability to implement AI governance, model explainability, privacy, and responsible AI practices
Honeywell Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Honeywell and has not been reviewed or approved by Honeywell.
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Retirement Support — Retirement benefits are anchored by a strong 401(k) match with clear vesting and annual funding mechanics. Plan administration and education resources further reinforce long‑term savings support.
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Leave & Time Off Breadth — Time away provisions include company holidays, flexible vacation for many exempt roles, and paid sick time. These policies contribute meaningful breadth beyond base pay.
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Parental & Family Support — Paid parental leave is available to all parents with flexible usage options, and certain family‑building supports are included. Birth mothers can coordinate leave with short‑term disability for extended coverage.
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The Company
What We Do
Honeywell is a Fortune 500 company that invents and manufactures technologies to address tough challenges linked to global macrotrends such as safety, security, and energy. With approximately 110,000 employees worldwide, including more than 19,000 engineers and scientists, we have an unrelenting focus on quality, delivery, value, and technology in everything we make and do.
