Job description

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. YOU MUST HAVE • 8 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 • Bachelor's / 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. US PERSON REQUIREMENT Due to compliance with U.S. export control laws and regulations, candidate must be a U.S. Person, which is defined as a U.S. citizen, a U.S. permanent resident, or have protected status in the U.S. under asylum or refugee status, or have the ability to obtain an export authorization. ABOUT HONEYWELL Honeywell International Inc. (NYSE: HON) invents and commercializes technologies that address some of the world’s most critical challenges around energy, safety, security, air travel, productivity, and global urbanization. We are a leading software-industrial company committed to introducing state-of-the-art technology solutions to improve efficiency, productivity, sustainability, and safety in high growth businesses in broad-based, attractive industrial end markets. Our products and solutions enable a safer, more comfortable, and more productive world, enhancing the quality of life of people around the globe. Learn more here: honeywell.com BENEFITS OF WORKING FOR HONEYWELL In addition to a performance-driven salary, cutting-edge work, and developing solutions side-by-side with dedicated experts in their fields, Honeywell employees are eligible for a comprehensive benefits package. This package includes employer-subsidized Medical, Dental, Vision, and Life Insurance; Short-Term and Long-Term Disability; 401(k) match, Flexible Spending Accounts, Health Savings Accounts, EAP, and Educational Assistance; Parental Leave, Paid Time Off (for vacation, personal business, sick time, and parental leave), and 12 Paid Holidays. For more information: benefits.honeywell.com The application period for the job is estimated to be 40 days from the job posting date; however, this may be shortened or extended depending on business needs and the availability of qualified candidates. Posting date: 5/21/2026 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 report to our Sr Data Engineering Manager and work from our Atlanta, GA location on a hybrid basis.

Requirements

Must-haves

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

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlllmreactmachine learningpythonci/cdapiazuregorustkubernetesagile

Platform: oracle-cloud
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Paid Time Off

Parental Leave