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À propos
Design and implement scalable architectures for processing high-volume IoT telemetry data, ensuring reliable data capture for AI/ML workloads. Build and maintain data pipelines throughout the AI product lifecycle, from training data preparation to inference. Develop and enhance RAG (Retrieval Augmented Generation) systems, focusing on vector databases and efficient retrieval mechanisms. Lead the architecture and development of scalable data platforms, particularly on Databricks. Integrate GenAI capabilities into data workflows and applications. Optimize data processing for performance, cost efficiency, and reliability at scale. Create robust data integration solutions merging industrial IoT data with enterprise data sources.
DataOps:
Adopt DataOps practices to ensure continuous integration and delivery of data pipelines for AI solutions. Design and maintain automated testing frameworks for data quality and model performance monitoring. Develop self-service data assets that empower data scientists and ML engineers. Ensure automated documentation for data lineage and model provenance.
Collaboration & Innovation:
Work closely with ML engineers and data scientists to create efficient data workflows. Mentor team members and provide leadership in resolving complex data engineering challenges. Establish data engineering best practices with a focus on modular design and reusable frameworks. Drive projects to completion within an agile setting amid evolving requirements in the fast-paced AI landscape.
Qualifications You Must Have: At least 5 years of experience building production data pipelines in Databricks with TB scale data. Comprehensive experience in implementing medallion architecture (Bronze/Silver/Gold) using Delta Lake and Delta Live Tables. Strong proficiency in PySpark for distributed data processing. Experience with cloud platforms such as Azure and GCP, particularly in AI/ML-driven workflows. Familiarity with CI/CD practices using Databricks Asset Bundles, GitHub Actions, and DataOps methodologies. Hands-on experience with RAG applications and vector databases, including frameworks like LangChain. A natural analytical mindset with the ability to optimize pipelines and debug distributed systems. We Value: Experience in building RAG and agentic architecture solutions. Expertise in real-time data processing frameworks. Knowledge of MLOps and experience with AI model deployment pipelines. Experience with time-series databases and IoT data modeling. Familiarity with containerization and orchestration tools. Strong background in data quality for AI training data. Experience with distributed teams and cross-functional collaboration. Understanding of data security and governance for AI systems. Experience in analytics projects with Agile methodologies. Benefits of Working for Honeywell We provide a competitive salary along with a comprehensive benefits package, including employer-subsidized medical, dental, vision, and life insurance, short-term and long-term disability, 401(k) matching, and flexible spending accounts. Additional perks include parental leave, paid time off, and 12 paid holidays. About Honeywell Honeywell International Inc. is dedicated to addressing critical global challenges through innovative technology solutions. As a software-industrial leader, we focus on enhancing efficiency, productivity, and safety in industrial markets with our flagship offering, Honeywell Forge. Join us and participate in building a smarter and more sustainable future. Honeywell is an equal opportunity employer, considering all qualified applicants without regard to personal attributes. Candidates must be U.S. Persons as defined by U.S. export control laws.
Compétences linguistiques
- English
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