Job Summary

NetApp is seeking an Machine Learning Engineer to join the Data Services organization. The overarching vision of this organization is to empower organizations to effectively manage and govern their data estate and build cyber-resiliency while accelerating their digital transformation journey. To get to this vision, we will embark on an AI-first approach to build and deliver a world-class suite of data services. As a key ML engineer in this initiative, the candidate will be responsible for independently deploying scalable AI/ML-based solutions, leveraging advancements in AI to solve real-world challenges in the domains of data protection and cybersecurity. The candidate will possess deep expertise in using modern AI/ML systems to ship impactful products to production. This is going to be a challenging and a fun role in one of the most exciting roles in the industry today.

Job Requirements

• Lead the development and deployment of AI/ML systems for Data protection with techniques from the realm of classical Machine learning, Generative AI and AI agents.
• Develop scalable data pipelines for various AI/ML-driven solutions from building curated data pipelines, setting up automated evals, adopting latest and greatest inferencing platforms for rapid iterations.
• Collaborate with data scientists and engineers to integrate AI into the broader products at NetApp. Effectively communicate complex technical artifacts to both technical and non-technical audiences.
• Work with a great deal of autonomy and proactively bring open-source AI innovations into our research and experimentation roadmap. Ensure scalability, reliability, and performance of AI models in production environments.
• Have a customer focus mindset and build AI/ML products that delight our customers.
• Represent NetApp as an innovator in the machine learning community and promote the company's product capabilities in industry/academic conferences.

Required and Preferred Qualification

• Master’s degree in computer science / applied mathematics / statistics / data science or equivalent experience.
• 3+ years of experience in building MLOps pipelines, CI/CD pipelines, and ML systems lifecycle management.
• Strong knowledge of optimizing and shipping machine learning and deep learning models to production.
• Proficiency in Python, SQL and at least one cloud platform (AWS, Azure or GCP).
• Excellent communication and collaboration skills, with demonstrated ability to work effectively with cross-functional teams and stakeholders of an organization

Preferred Qualification

• 1+ years of experience in data engineering, including building and optimizing data pipelines and architectures.
• Solid understanding of data science fundamentals and model evaluations, including supervised and unsupervised machine learning algorithms (both machine learning and deep learning).
• Good understanding of cyber-security and data protection frameworks.
• Experience of representing your work or company at AI/ML conferences. 
• Active GitHub profile showcasing relevant open-source AI/ML projects or Kaggle achievements.