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.

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 Lang Chain, Lang Graph, 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.

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 Lang Graph, MLflow, and custom orchestration layers for multi-agent AI systems.

• Develop and integrate AI workloads using ML-Ops and tracing tools like Lang Smith.

• 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.