Honeywell

Full Stack AI Platform Engineer responsible for designing, building, and scaling enterprise AI/ML platforms, focusing on IoT data pipelines, LLM orchestration, RAG services, and deploying models to edge devices. The role involves developing Python APIs for inference, managing ML platform services, building CI/CD for edge deployments, implementing ML orchestration workflows, and integrating AI workloads.

This role involves designing, building, evaluating, and integrating AI/ML models and analytical components, with a focus on generative AI, LLMs, and multi-modal embeddings for security applications. The data scientist will be responsible for data engineering, developing new generative AI systems, and deploying analytical systems that improve security outcomes, potentially involving agentic patterns and frameworks.

AI Technical Program Manager for GBS Finance at Honeywell, leading the development and scaling of autonomous and agentic AI systems for business workflows. The role requires a hybrid mindset of senior program leadership and technical depth, focusing on governance, metrics, architecture, and continuous improvement within a regulated finance environment.

This role focuses on assessing the security of GenAI/LLM-enabled systems, including RAG and agentic workflows. Responsibilities include identifying vulnerabilities like prompt injection, data exfiltration, and tool abuse, evaluating guardrails, and reporting findings to engineering teams for remediation. The role requires hands-on penetration testing experience across various domains, with a specific emphasis on GenAI security.

Honeywell is seeking an Advanced Data Scientist to own end-to-end data science and machine learning solutions, from problem formulation to production deployment. This role requires expertise in machine learning, data engineering, MLOps, cloud platforms, and technical leadership, working with stakeholders to design scalable data and ML systems. Responsibilities include building NLP and GenAI applications, implementing RAG pipelines, prompt engineering, and evaluating LLM systems, alongside developing classical ML and deep learning models. The role also involves MLOps for the full ML lifecycle, deployment using APIs, and monitoring production models on cloud platforms (AWS/Azure/GCP).

This role focuses on developing and integrating AI/ML models and generative AI systems, including LLMs and multi-modal embeddings, to enhance security insights and product capabilities. It involves data engineering, evaluating third-party solutions, and deploying analytical systems for forensic and real-time security outcomes.

Data Scientist II role focused on leveraging Generative AI, OCR, and Computer Vision to build and deploy models and end-to-end pipelines, with an emphasis on cloud platforms and MLOps within the life sciences domain.

Lead Solutions Architect for Honeywell's Forge Digital COE, focusing on industrial IoT, smart buildings, and sustainability. The role involves owning end-to-end solution architecture for Forge-aligned digital platforms, defining cloud-native standards on Azure, integrating building/energy systems, and driving R&D for Gen AI and Agentic AI use cases. Responsibilities include architectural governance, mentoring, and tracking emerging technologies.

Director of Cybersecurity Software Engineering & AI responsible for leading the design, development, and delivery of innovative software solutions that modernize cybersecurity operations, eliminate inefficiencies, and amplify the capabilities of the cybersecurity organization through intelligent automation and AI-driven tooling. This individual will lead a small, high-performing engineering team in designing, building, and maintaining secure, scalable, and intelligent software solutions that directly advance the organization’s cybersecurity mission and operational efficiency objectives.

This role focuses on applying AI-driven operational optimization solutions for industrial customers (Chemical, Oil & Gas, etc.) using Honeywell's Experion DCS system. The engineer will act as a Subject Matter Expert, bridging process engineering and digital/analytical skills. Responsibilities include leading customer workshops to define problems, analyzing process data, validating AI model strategies and outcomes with Data Scientists, and assisting with deployment and testing of solutions. The role requires strong process engineering knowledge, experience with DCS systems, and the ability to translate between process operations and data science.

Software Engineer focused on quality and testing for cloud-based products, with specific experience in Agentic AI, AI testing, and test automation within an enterprise context.