As a Senior Advanced AI Engineer, you will design, develop, and deploy AI-driven solutions for smart buildings and industrial automation systems. Your primary focus will be building advanced ML models, integrating them into real-world control environments, and driving innovation across HVAC, lighting, security, and energy optimization. You will collaborate cross‑functionally, mentor junior engineers, and influence multiple projects with your technical expertise.

MUST HAVE

• Technical Expertise Strong proficiency in Python and ML libraries such as Py Torch, Tensor Flow, JAX, XGBoost, and scikit‑learn.
• Experience with Kubernetes, Databricks, or comparable platforms.
• Familiarity with CI/CD practices for AI/ML workflows.
• Working knowledge of Py Spark for data exploration and pipeline contributions.
• Strong debugging, profiling, and performance engineering skills in Python.
• AI/ML Knowledge Expertise in one or more key domains: NLP, time-series forecasting, computer vision, or reinforcement learning.
• Ability to build models with noisy or sparsely labeled datasets.
• Experience using MLflow or similar tools for tracking, reproducibility, and model registry.
• Knowledge of converting models for production inference (Torch Script, ONNX).
• Experience with model performance optimization (e.g., quantization, latency tuning).
• Working knowledge of applying, fine‑tuning, and optimizing foundation models for domain-specific tasks across text, vision, or time‑series modalities.
• Ability to make informed accuracy–cost trade-offs during model design.
• Innovation Skills Ability to identify emerging AI trends and translate them into practical solutions.
• Experience in rapid prototyping, proof‑of‑concept development, and technology scouting.
• Strong problem‑solving mindset with a focus on creative and disruptive solutions.
• Cloud & Edge Computing Knowledge of AI/ML offerings from major cloud providers (Azure, GCP, or AWS).
• Experience deploying AI/ML solutions on edge devices (e.g., NVIDIA Jetson) is a plus but not mandatory.
• Education & Experience Bachelor’s degree in Computer Science, Electrical Engineering, or a related field; Master’s degree preferred.
• Bachelor’s + 6 years of relevant AI/ML experience
• Master’s + 4 years of relevant AI/ML experience
• PhD + 2 years of relevant AI/ML experience

WE VALUE

• Experience optimizing deep learning models for NVIDIA Jetson–based edge systems.
• Experience contributing to platform‑agnostic AI/ML solutions.
• Proven end‑to‑end ownership of the ML lifecycle, including training, deployment, and feedback loops.
• Experience with smart building platforms, SCADA systems, or energy management solutions.
• Demonstrated success delivering innovative AI solutions within automation domains.

In addition to a competitive salary, leading-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 visit: Benefits at Honeywell

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. Job Posting Date: 03/27/2026.

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.

• AI Solutions Design & Integration Design and integrate AI/ML models into Building Management Systems (BMS) and Industrial Control Systems (ICS), including SCADA and PLC environments.
• Implement real‑time API–based and batch‑inference workflows.
• Develop model feedback loops to support continuous learning and performance improvement.
• Build algorithms for real‑time decision‑making using sensor, IoT, and industrial process data.
• Data Engineering Partner with Data Engineering teams on ETL workflows and data preparation for large‑scale building and industrial datasets (e.g., HVAC telemetry, energy consumption, machine performance).
• Contribute to feature engineering and ensure data readiness for modeling
• Support the development of training pipelines that leverage model registries and tracking systems.
• Innovation & Research Explore emerging technologies such as generative AI, digital twins, multimodal foundation models, and autonomous control systems.
• Lead proof‑of‑concept initiatives and mentor junior engineers through early‑stage experimentation.
• Translate innovative concepts into practical solutions for automation and building intelligence.
• Performance Optimization Collaborate with MLOps teams to optimize real-time inference across platforms (AKS, GKE, on‑prem microk8s).
• Work with production‑ready inference runtimes such as vLLM, ONNX Runtime, and NVIDIA Triton.
• Contribute to model conversion, quantization, and optimization for efficient inference.
• Partner with platform engineers on deployment strategies, scalability, and monitoring.
• Compliance & Security Ensure all AI solutions comply with cybersecurity standards and industrial safety protocols.
• Maintain training and inference repositories to meet corporate and industry security requirements.