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

Responsibilities

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.

Qualifications

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.

Honeywell Technologies is a global, pure-play automation company with a legacy of innovating to help solve the world’s most mission-critical challenges, enhancing the quality of life for people and communities around the world. We serve the building, industrial and process sectors with a broad portfolio of services, solutions and products, underpinned by our Honeywell Technologies Accelerator operating system and Honeywell Technologies Forge intelligence layer. By combining the deep domain expertise of our more than 50,000 employees with decades of data from our global installed base, we are uniquely positioned to lead the industrial sector’s transition from automation to autonomy.