Machine Learning Engineer
netapp
Job Description
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.
Job Expectations
- The position is a Hybrid position, and the candidate is expected to work in NetApp Bangalore office at least two days a week.
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.