Sr Advanced AI Platform Engineer

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.

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.

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. You will report to our Sr Data Engineering Manager and work from our Atlanta, GA location on a hybrid basis.

• Note: for the first 90 days, new hires must be prepared to work onsite 100% M-F.

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.