Job description
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.
- 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.
- 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.