NVIDIA is hiring a Senior Data Engineer on our unified LLM MLOps platform by developing ML data pipelines for a wide range of diverse use cases. NVIDIA is in a unique position: we are developing AI-based products across multiple domains. Our vision is to bring to bear the MLOps platform we developed for Autonomous Vehicles and apply it to other use cases: NLP, speech, recommendation systems, computer vision, healthcare. To achieve this, we will embed ourselves into ML teams to help them develop their MLOps pipeline on to a single methodology that uses that unified platform, recognize what is common to all use cases and what is domain-specific and develop MLOps tools and features. The members of that team will realize the MLOps strategy for NVIDIA and understand both MLOps production pipelines and MLOps infrastructure in order to help us create what could be the best and most comprehensive MLOps platform in the world.What you'll be doing:Support machine learning teams that deliver some of the most incredible large language model based products at NVIDIAPerform difficult or complex analysis, design and programming involving multi-project leadership and broad responsibility in support of new and existing Data Engineering Projects and production support activitiesCollaborate with data strategy partners to caters to LLM data analytics requirementsLead and perform the design, development, implementation and maintenance of complex Data Store/ Data Lake/Lake house and Data warehousing systems and data-intensive solutions that are scalable, optimized, and fault-tolerantApply data modeling skills leveraging solid understanding and experience in Cloud technologies and applicationsIntegrate new sources smoothly; know the pain points in Data integration across SaaS applications and implement the best solution that fits the organizationConstantly monitor operations, tune for better performance and utilizationPromote Culture of Data Safety: Demonstrates commitment to personal responsibility and value for data safety; communicates data safety concerns; uses and promotes safe data handling behaviorsWhat we need to see:You have a Bachelors or Masters in Computer Architecture, Computer Science, Electrical Engineering or equivalent experience.5+ years of Work Experience in developing production systems.Demonstrated the ability to develop data-first, production machine learning pipelines.Mastery and hands-on experience with Data Engineering technologies and scripting languagesThorough understanding and experience in Data Lake, Lake House and Data Warehousing ArchitectureRecommend, architect and implement Data Lake/Lake house/ Data Warehouse solution with a set of available cloud tools and programmingHands-on experience and expertise in Advanced SQL, Advanced Python programming, AWS Services (such as S3, Redshift, Glue ETL, IAM, DMS, Appflow, VPC and others), SnowFlake, KAFKA, Airflow, Oracle Cloud and other open source toolsExperience in writing reusable complex Python/PySpark scripts for ELT, Business Logic OR APIsMastery of relational, NoSQL or NewSQL database systemsExpertise in working with unstructured, structured, and semi-structured dataExperience in designing and implementing tight data security at various levelsExperience with DataOps and related set of practices, processes and technologiesWith highly competitive salaries and a comprehensive benefits package, Nvidia is widely considered to be one of the technology industry's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working with us and our engineering teams are growing fast in some of the hottest state of the art fields: Deep Learning, Artificial Intelligence, and Large Language Models. If you're a creative engineer with a real passion for robust and enjoyable user experiences, we want to hear from you.NVIDIA is committed to encouraging a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
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