AI/ML Engineer
At a glance
Mid-level Machine Learning Engineering role at NetApp. Bangalore.
Pay not stated Machine Learning Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Create AI agents that can reason, plan, use tools and complete tasks across multiple steps
- Design LLM systems for efficient token use and inference, using prompt frameworks and caching
- Build RAG workflows with hybrid search, re-ranking, agent-led retrieval and changing knowledge sources
- Run model training, evaluation, deployment and monitoring through scalable MLOps pipelines
- Adapt foundation models for storage, cloud infrastructure and enterprise operations use cases
What you bring
- At least five years of related experience, including hands-on production deployment of AI/ML solutions
- A bachelor's or master's degree in a relevant engineering or data field, or equivalent experience
- Python proficiency, plus working familiarity with Go and front-end frameworks such as React or Streamlit
- Practical expertise with AI agents, LLMs, RAG, vector databases, MLOps and containerized deployment
Who this fits
This individual-contributor role suits a mid-career engineer who has shipped AI/ML systems and can work across the path from problem definition to deployment and early customer support. The role is based in Bangalore, India. Storage or cloud experience is a plus, and the work values clear communication, responsible AI, quality and meeting deadlines.
From the employer
Job Summary
NetApp's Chief Platform and Technology Office (CPTO) is seeking AI/ML Engineers to design and build cutting-edge AI/ML-powered incubation projects that drive NetApp's technology strategy and business transformation.
Job Requirements
- Design and build intelligent AI agents capable of reasoning, planning, tool use, and multi-step task execution.
- The ideal candidate brings 5–8 years of hands-on experience deploying AI/ML solutions in production, with deep expertise in AI agents, LLM integration and optimization, RAG architectures, and modern ML frameworks.
- Proficiency in Python is required, along with working knowledge of Go and front-end frameworks (React/Streamlit/Gradio).
- Architect and optimize LLM-powered systems including token optimization, cost-efficient inference, prompt engineering frameworks, and caching mechanisms.
- Candidates should have strong experience with vector databases, MLOps practices, containerized deployment (Docker/Kubernetes), and cloud AI/ML services (AWS SageMaker, Azure ML, GCP Vertex AI). A solid foundation in classical ML/DL fundamentals, distributed systems, and software engineering best practices is essential.
- 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 that shape NetApp's AI/ML roadmap and long-term product vision.
- Explore unconventional AI architectures — including multi-modal AI, edge inference, and distributed training.
- Collaborate cross-functionally with engineers, data scientists, and product teams from problem definition through deployment and early customer support.
- Stay current with the evolving AI/ML landscape and drive adoption of emerging models, frameworks, and techniques that deliver business value.
- Experience in the storage/cloud domain is a strong plus. We value strong communicators who are passionate about responsible AI, quality, and meeting deadlines.
Education
• IC - Typically requires a minimum of 5 years of related experience.
• Bachelor/Master of Engineering in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field or equivalent experience.
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