Data and Applied Scientist

Job Summary

NetApp is seeking an AI 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 world-class suite of data services. As a key AI engineer in this initiative, the candidate will be responsible for independently developing and deploying LLM-based solutions, leveraging advancements in AI to solve real-world challenges in the domains of data governance and compliance. 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 governance 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-focused 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.

Education And Qualification

• Master’s degree in computer science / applied mathematics/statistics/data science or equivalent experience.
• Strong expertise in data science fundamentals and model evaluations. Solid understanding of supervised and unsupervised machine learning algorithms (machine learning and deep learning).
• 4-8 years of experience of optimizing and shipping machine learning and deep learning models to production.
• Demonstrated proficiency in using large language models, prompting techniques, fine-tuning and knowing when not to use LLMs.
• Proficiency in Python and at least one modern ML framework such as PyTorch / Tensorflow or transformers.
• Excellent communication and collaboration skills, with a demonstrated ability to work effectively with cross-functional teams and organizational stakeholders.
• Curiosity and tinkering with AI agents or multi-agent systems with agentic AI frameworks (Langgraph, LlamaIndex, crewai, smolagents etc.).
• Understanding of data governance, security policies, and compliance frameworks.
• Applied knowledge of MLOps / LLMOps practices, CI/CD pipelines, cloud platforms and ML systems lifecycle management.
• Experience of representing their work or company in AI/ML conferences. Publications or contributions to AI/ML community related to NLP or data governance.
• Active GitHub profile showcasing relevant open-source AI/ML projects or Kaggle achievements