Data and Applied Scientist

Job Description

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 companys 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