Software Engr II

Job description

As a Full Stack AI Platform Engineer here at Honeywell, you will design, build, and scale AI systems end-to-end — from high-throughput IoT streaming pipelines and knowledge graph infrastructure, through LLM orchestration and RAG services, to the React-based interfaces that surface autonomous insights to plant engineers, facility managers, and OT security analysts. We are seeking a Full Stack AI Platform Engineer to join our Data Engineering, AI & ML Platform team. This role is central to designing, building, and scaling the enterprise AI/ML platform that powers intelligent automation across a global portfolio. You will work at the intersection of data engineering, machine learning operations, and edge AI — building production-grade infrastructure that processes billions of IoT events from building management systems, deploys models to edge devices, and enables AI-driven applications including predictive diagnostics, energy monitoring, and RAG-based knowledge systems. This is a high-impact individual contributor role for someone who thrives in ambiguity, ships production systems, and can operate across the full stack from cloud-native platforms to edge GPU hardware Key Responsibilities AI/ML Platform Engineering • Develop high-performance, production-ready Python APIs using FastAPI to serve as the primary interface for on-device model inference • Design, build, and maintain enterprise AI/ML platform services on multi-cloud infrastructure including model deployment, serving and experiment tracking. • Build robust CI/CD stacks to automate the testing of inference logic and the deployment of API services to edge devices. • Implement ML orchestration workflows using LangGraph, MLflow, and custom orchestration layers for multi-agent AI systems. • Develop and integrate AI workloads using ML-Ops and tracing tools like LangSmith. • Design and implement automated data processing pipelines within FastAPI to handle real-time sensor or image inputs for the model. • Bridge the gap between research and deployment by converting code from experimental into modular, maintainable Python packages. Edge AI & Inference • Ability to integrate and run pre-built AI models on local hardware using standard industry runtimes. • Skilled at building the software logic required to process data inputs and handle model outputs efficiently. • Expert at developing Python-based services and automating their deployment to devices via standardized pipelines. • Capable of monitoring and optimizing software to run reliably within strict memory and hardware limitations. • Experience deploying containerized models from Azure to edge devices using Azure IoT Edge or managed online endpoints Data & Knowledge Engineering • Experience building pipelines to structure, clean, and store data for model training or real-time retrieval (RAG) on edge devices • Ability to convert experimental data processing logic from notebooks into production-ready Python modules. • Design automated workflows to collect, label, and manage datasets, ensuring high-quality data is available for continuous model improvement. Production Operations & Reliability • Own platform reliability for AI services serving multiple business units. • Implement observability, monitoring, and alerting for ML pipelines and inference services. • Drive cost optimization across data platform workloads, cloud compute, and storage infrastructure. • Proficient in using Azure Machine Learning Studio to manage the full lifecycle of models, including registration, versioning, and monitoring. YOU MUST HAVE • Bachelor's degree from an accredited institution in a technical discipline such as science, technology, engineering, mathematics. • 3 plus years of experience in software engineering, data engineering, or ML platform engineering. • Strong proficiency in Python and at least one systems language (Python, Go, Rust, C++). • Deep hands-on experience with cloud-native data platforms (Databricks, BigQuery, Azure Data Lake, Kubernetes). • Production experience building and deploying ML/AI pipelines including model serving, feature engineering, and experiment tracking. • Experience with LLM application frameworks such as LangChain, LangGraph, and Langsmith or equivalent agentic AI orchestration tools. • Experience with edge AI deployment on NVIDIA Jetson or similar embedded GPU platforms. • Experience with knowledge graphs, ontology engineering, or semantic web technologies. WE VALUE • Advanced degree in Computer Science, Artificial Intelligence, or related field. • Background in building management systems, HVAC, energy management, or industrial IoT domains. • Strong leadership and management skills. • Experience working in an agile development environment. • Proven ability to drive successful cloud development projects and initiatives. • Ability to work in a fast-paced and dynamic environment. • Attention to detail and excellent problem-solving capability.