RAG-Powered AI Search Engineer

Activeloop; Mountain View (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Activeloop in Mountain View is seeking an AI Engineer who will design, develop, and deploy advanced AI search and retrieval systems that leverage RAG techniques to solve complex information access challenges.

You will collaborate with software engineers, customers, and business stakeholders to build scalable, low-latency search solutions, optimize vector databases, and evaluate performance with rigorous metrics.

Qualifications

• Master's or PhD in CS/ML/Statistics or related field.
• Proven production experience with ML models and cloud platforms.
• Strong knowledge of information retrieval, RAG, and search systems.
• Experience with distributed training, vector databases, and scaling.
• Proficiency in Python and at least one of C++ and modern ML libraries.

Responsibilities

• Design, develop, and deploy AI search and retrieval systems using RAG.
• Collaborate with engineers, customers, and stakeholders to deliver AI search solutions.
• Lead design and implementation of retrieval systems like Deep Memory by Activeloop.
• Develop and optimize search algorithms, including semantic and hybrid search.
• Integrate and optimize vector storage and indexing for high-dim embeddings.
• Design query processing pipelines and evaluate system performance with metrics.
• Ensure scalability, low latency, and real-time capabilities for large datasets.

Skills

Python

Machine Learning

Information Retrieval

Distributed Training

Cloud Platforms

Education

Master's or PhD in Computer Science / ML / Statistics

Tools

TensorFlow

PyTorch

Llama Index

LangChain

Deep Lake

Job description

Activeloop in Mountain View is seeking an AI Engineer who will design, develop, and deploy advanced AI search and retrieval systems that leverage RAG techniques to solve complex information access challenges.

You will collaborate with software engineers, customers, and business stakeholders to build scalable, low-latency search solutions, optimize vector databases, and evaluate performance with rigorous metrics.