Sr Advanced AI Platform Engineer

Honeywell•Atlanta, GA

8h•Hybrid

About The Position

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.

Requirements

Full Stack AI Platform Engineer
Data Engineering, AI & ML Platform team
designing, building, and scaling the enterprise AI/ML platform
design, build, and scale AI systems end-to-end
high-throughput IoT streaming pipelines
knowledge graph infrastructure
LLM orchestration and RAG services
React-based interfaces
intersection of data engineering, machine learning operations, and edge AI
production-grade infrastructure
processes billions of IoT events from building management systems
deploys models to edge devices
AI-driven applications including predictive diagnostics, energy monitoring, and RAG-based knowledge systems
individual contributor role
thrives in ambiguity
ships production systems
operate across the full stack from cloud-native platforms to edge GPU hardware
report to our Sr Data Engineering Manager
work from our Atlanta, GA location on a hybrid basis
for the first 90 days, new hires must be prepared to work onsite 100% M-F
Develop high-performance, production-ready Python APIs using FastAPI
Design, build, and maintain enterprise AI/ML platform services on multi-cloud infrastructure
Build robust CI/CD stacks
Implement ML orchestration workflows using LangGraph, MLflow, and custom orchestration layers
Develop and integrate AI workloads using ML-Ops and tracing tools like LangSmith.
Design and implement automated data processing pipelines within FastAPI
Bridge the gap between research and deployment by converting code from experimental into modular, maintainable Python packages.
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
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.
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.

Responsibilities

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.
Integrate and run pre-built AI models on local hardware using standard industry runtimes.
Build the software logic required to process data inputs and handle model outputs efficiently.
Develop Python-based services and automate their deployment to devices via standardized pipelines.
Monitor and optimize software to run reliably within strict memory and hardware limitations.
Deploy containerized models from Azure to edge devices using Azure IoT Edge or managed online endpoints
Build pipelines to structure, clean, and store data for model training or real-time retrieval (RAG) on edge devices
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.
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.
Manage the full lifecycle of models, including registration, versioning, and monitoring, using Azure Machine Learning Studio.