Job Summary

As a Data Architect of the Active IQ Analytics practice at NetApp, you will play a crucial role in the development and maintenance of our data infrastructure. You will be responsible for architecting machine learning systems and building data pipelines and ML models. You will be responsible for designing, building, and optimizing our data architecture, as well as managing the flow of data throughout the organization. Your expertise in SQL Server/Oracle and MongoDB will be essential in ensuring the integrity, availability, and performance of our data systems.

You will be part of a highly skilled technical team and working closely with the team of senior software developers and technical directors. You will be responsible for contributing and aligning to system level application architecture that includes high level design, coding standards, and development and testing of code. The software applications you build will be used by our internal sales team, partners and customers.

• Lead the design and implementation of ML systems for Data governance area with techniques such as classical Machine learning, Generative AI models and AI agents.
• Ensure scalability, reliability, and performance of AI models in production environments.
• Oversee ML design reviews, create best practices and playbooks for end-to-end ML systems in production.
• Collaborate with data engineers to develop scalable data pipelines for various AI/ML-driven solutions from building curated data pipelines, ML feature pipelines and deployment services.
• Work with a great deal of autonomy and be the technical thought leader in data governance product area. creating a forward-looking vision with clear direction.
• Effectively communicate complex technical artifacts to both technical (engineers & scientists) and non-technical audiences.
• Work closely with cross-functional teams including business stakeholders to innovate and unlock new use-cases for our customers that is driven through data intelligence.
• Participate in cross-functional meetings, workshops, and planning sessions to ensure data engineering activities support the overall objectives across data services and platform initiatives.
• Coaching and leadership for data scientists and the broader cross-functional team, helping influence and develop their skills and capabilities by fostering a culture of innovation and continuous learning.
• Experience as a data and machine learning engineer, with a track record of building data/feature pipelines and shipping successful products with AI/ML & NLP capabilities at scale. Recent focus on experimenting & deploying LLMs to production is a bonus.
• Solid understanding of supervised and unsupervised machine learning algorithms and experience shipping them in production.
• Strong Proficiency in Python, modern ML frameworks (PyTorch, transformers) and cloud platforms.
• Applied knowledge of MLOps practices, CI/CD pipelines and ML model lifecycle management.

• Minimum of 10 years of related experience and Master's or Bachelor's in computer science, Engineering, Applied Mathematics/Statistics/Data Science or equivalent skills.
• Experience as a data and machine learning engineer, with a track record of building data/feature pipelines and shipping successful products with AI/ML & NLP capabilities at scale. Recent focus on experimenting & deploying LLMs to production is a bonus.