Honeywell

Senior Data Engineer to design, build, and scale cloud-native data and AI platforms on Azure using Databricks, focusing on data engineering, lakehouse architecture, and AI/ML data pipelines to support advanced analytics and ML use cases. The role involves leading data initiatives, collaborating with data scientists and ML engineers, and contributing to the data and AI strategy.

Senior Data Engineer at Honeywell focused on building scalable data pipelines and architectures for industrial AI solutions, with an emphasis on IoT data, RAG systems, and supporting the AI product lifecycle. The role involves significant work with Databricks, PySpark, and cloud platforms (Azure, GCP).

Lead Data Engineer on the Industrial AI & Data Platforms team, responsible for architecting and owning data foundations for physical AI at scale, including IoT sensor telemetry and Generative AI pipelines. This role involves technical leadership, building AI-ready data products like vector stores and RAG workflows, and mentoring engineers.

Senior Advanced Data Scientist at Honeywell in Bengaluru, India, focusing on machine learning, statistical modeling, and data analysis. The role involves leading complex projects, mentoring junior scientists, and contributing to the strategic vision of the Data Science team, translating business needs into data science solutions.

Hardware Engineer II role at Honeywell focused on delivering data-driven insights for global markets, cost structures, and manufacturing ecosystems. The role involves designing, developing, and deploying advanced analytical, statistical, and machine learning models, analyzing market and economic trends, performing cost analysis, and leveraging manufacturing domain expertise. It requires strong project management, collaboration, and leadership skills, with a focus on driving process improvements and influencing the strategic direction of the Data Analytics team. Experience with SAP/ERP and PLM systems is also mentioned.

This role involves applying data analysis, machine learning, and statistical modeling to solve business problems, working with senior data scientists and cross-functional teams to develop data-driven solutions. The goal is to leverage data to optimize processes, reduce costs, and identify growth opportunities, positioning Honeywell at the forefront of data-driven innovation.

Internship role focused on supporting proposal and estimating teams in process automation, with a specific emphasis on identifying and implementing AI in daily engineering activities. The role involves assisting estimators, looking after technical details, and participating in internal negotiations.

Lead R&D Engineer/Scientist focused on developing process performance data analytics and connected digital solutions for industrial applications, optimizing commercial operations and profitability. The role involves leading technology development programs, coordinating cross-functional teams, and identifying new approaches in data science and machine learning.

This role focuses on designing, developing, and maintaining scalable software solutions, with a specific emphasis on leveraging Microsoft Azure services and contributing to GenAI enabled solutions through experimentation and proof-of-concept development. The role involves full-stack development, API design, microservices architecture, and CI/CD practices, while also exploring and integrating GenAI capabilities.

This role focuses on designing, developing, and maintaining scalable software solutions, with a specific emphasis on leveraging Microsoft Azure services and contributing to GenAI enabled solutions through experimentation and proof of concept development. The role involves full-stack development, API design, cloud-native architecture, microservices, and CI/CD practices, while also exploring and integrating GenAI capabilities.

Develops and implements advanced data analytics models and algorithms using machine learning, statistical modeling, and data analysis techniques to solve complex business problems and provide actionable insights. Manages end-to-end data science projects, mentors junior colleagues, and contributes to the strategic vision of the Data Science team. Focuses on optimizing processes, reducing costs, and identifying growth opportunities.

The Principal Software Architect will lead the design, development, and operationalization of buildings ontologies and taxonomies to power data interoperability, analytics, and intelligent systems. This role involves working with domain modeling, semantic technologies, and standards, and collaborating with domain experts, data engineers, platform architects, and product teams. Key responsibilities include designing and maintaining industrial ontologies, aligning with industry standards (Brick Schema, Project Haystack, ASHRAE 233P, IFC), defining concept hierarchies, and leveraging LLMs for ontology population and entity extraction. The role also involves mapping concepts across heterogeneous data sources, ensuring interoperability, and defining best practices for ontology governance. Strategic responsibilities include defining the technical vision for the enterprise semantic and knowledge graph platform and establishing architectural standards. The role requires deep, hands-on experience in ontology engineering, strong knowledge of specific standards, and expertise in semantic web technologies and graph databases.