About The Role

Honeywell is seeking an experienced Advanced Software Engineer (Python AI/GenAI) to design, develop, and deploy enterprise-scale AI-powered applications. The ideal candidate should possess strong expertise in Python development, Machine Learning, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and cloud-native architectures.

You will collaborate with product teams, architects, data scientists, and business stakeholders to build intelligent solutions that drive automation, productivity, and innovation across Honeywell products and platforms.

Design, develop, and maintain scalable AI applications using Python.
Build and deploy Generative AI solutions leveraging LLMs such as GPT, Llama, Claude, and Azure OpenAI.
Develop RAG-based systems using vector databases and semantic search techniques.
Create AI agents and multi-agent workflows using LangChain, LangGraph, CrewAI, or similar frameworks.
Design and implement REST APIs and microservices.
Integrate AI models with enterprise applications and cloud platforms.
Collaborate with Data Science teams for model training, fine-tuning, evaluation, and deployment.
Build CI/CD pipelines and automate model deployment workflows.
Ensure AI solutions comply with security, governance, and responsible AI guidelines.
Mentor junior engineers and participate in architecture reviews and technical leadership activities.
Drive best practices for software engineering, code quality, testing, and observability.
Bachelor's or Master's degree in Computer Science, Engineering, AI, Data Science, or related discipline.
6–9 years of software development experience.
Strong programming expertise in Python.
Hands-on experience with FastAPI, Flask, or Django.
Experience with Machine Learning frameworks such as PyTorch or TensorFlow.
Strong understanding of LLMs, Prompt Engineering, Fine-Tuning, Embeddings, and RAG architectures.
Experience with LangChain, LlamaIndex, LangGraph, or equivalent AI frameworks.
Knowledge of vector databases such as Pinecone, Milvus, FAISS, ChromaDB, or Weaviate.
Experience with PostgreSQL, MySQL, MongoDB, or other databases.
Familiarity with Docker, Kubernetes, Git, and CI/CD pipelines.
Experience working on Azure, AWS, or GCP cloud platforms.
Strong understanding of microservices architecture and API development.

Preferred Qualifications

Experience with Azure OpenAI Services.
Knowledge of MLOps tools such as MLflow, Kubeflow, SageMaker, or Azure ML.
Experience with AI Agent frameworks and autonomous workflows.
Exposure to Industrial Automation, IoT, Manufacturing Analytics, or Connected Products domains.
Understanding of AI evaluation, observability, guardrails, and governance frameworks.

Experience with Semantic Search, Graph RAG, and Knowledge Graphs.