About this role
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. As a Senior Advanced AI Engineer at Honeywell, you will be a driving force in designing, developing, and deploying end-to-end cloud and AI/ML solutions aimed at bringing autonomous capabilities to Honeywell products over the next decade. You will operate as a hands-on technical leader, working on everything from data pipelines to model optimization and drift detection, while mentoring junior team members to build a truly full-stack AI/ML practice.
Key Responsibilities
• Design and implement high-impact AI/ML models and workflows, ability to work on Cloud architectures and build solutions, ensuring scalability and reliability on cloud platforms such as Databricks, VertexAI, etc. • Collaborate with cross-functional teams (Data Engineering, ML Engineering, DevOps) to create holistic MLOps pipelines, leveraging frameworks such as MLflow and Kubeflow. • Conduct thorough reviews of ML models for performance, bias, and drift, proposing corrective actions. • Integrate AI (including TimeSeries, Computer Vision, NLP, GenAI/RAG/Agentic AI) solutions into existing Honeywell products, maintaining rigorous code quality standards. • Mentor junior engineers, promoting best practices in model development and deployment.
Qualifications & Experience
• Bachelor’s or Master’s degree in Computer Science, AI, or related technical field. • 6+ years of hands-on experience developing and deploying ML models in production. • Proven track record in advanced machine learning frameworks (e.g., TensorFlow, PyTorch). • Demonstrated expertise in MLOps tools and best practices (CI/CD, containerization, orchestration). • Strong Python skills, with exposure to additional languages (Scala, Java), considered a plus. Preferred Competencies
• Full-stack AI/ML experience (data ingestion through model deployment and maintenance). • Strong analytical mindset with a bias towards skeptical, data-driven decision-making. • Familiarity with cloud platforms (AWS, Azure, or GCP) for large-scale training and deployment. • Ability to communicate technical concepts to both experts and laypersons. • Knowledge of Agile or similar software development methodologies.