AI/ML Engineer

About the role

NetApp's Chief Platform and Technology Office (CPTO) is looking for AI/ML Engineers in Bangalore to design and build AI/ML-powered incubation projects that support NetApp's technology strategy and business transformation. The role involves building intelligent AI agents, optimizing LLM-powered systems and contributing to proof-of-concept projects.

Key responsibilities

• Design and build intelligent AI agents capable of reasoning, planning, tool use and multi-step task execution.
• Architect and optimize LLM-powered systems, including token optimization, cost-efficient inference, prompt engineering frameworks and caching.
• Build and scale advanced RAG pipelines with hybrid search, re-ranking, agentic retrieval and dynamic knowledge base integration.
• Develop and maintain MLOps pipelines for model training, evaluation, deployment and monitoring at scale.
• Fine-tune and customize foundation models for domain-specific use cases in storage, cloud infrastructure and enterprise operations.
• Build large-scale, fault-tolerant, low-latency distributed systems for real-time AI inference.
• Contribute to POCs and incubation projects, and explore architectures such as multi-modal AI, edge inference and distributed training.
• Collaborate with engineers, data scientists and product teams from problem definition through deployment and early customer support.

Required profile

• 5-8 years of hands-on experience deploying AI/ML solutions in production.
• Bachelor's or Master's degree in Computer Science, AI, Machine Learning, Data Science or a related field, or equivalent experience.
• Strong experience with vector databases, MLOps, containerized deployment and cloud AI/ML services.
• Solid foundation in classical ML/DL fundamentals, distributed systems and software engineering best practices.
• Experience in the storage/cloud domain is a strong plus; strong communication and a passion for responsible AI are valued.

Required skills

• Python (required); working knowledge of Go and front-end frameworks such as React, Streamlit or Gradio.
• LLM integration, RAG architectures and prompt engineering.
• Vector databases, Docker, Kubernetes, AWS SageMaker, Azure ML, GCP Vertex AI.

The company uses a hybrid working environment, and applications must be submitted through its company website.