Highlights
Design scalable pipelines, enhance retrieval accuracy, manage embeddings.
Description
Job Summary
pWe are seeking an AI Data / RAG Specialist to develop and oversee retrieval-based artificial intelligence systems using internal company data. The ideal candidate will design scalable pipelines that ensure accurate, context-aware responses through modern AI architectures.
Responsibilities
- Build and maintain Retrieval-Augmented Generation (RAG) pipelines
- Process, clean, and index structured/unstructured data
- Create and manage embeddings for efficient retrieval
- Integrate vector databases and retrieval systems
- Ensure high relevance and accuracy of AI-generated responses
- Optimize systems for speed, scalability, and performance
Required Skills
- Expertise in RAG architecture and embeddings
- Familiarity with vector databases and retrieval systems
- Data preprocessing and structuring abilities
- Possess Python proficiency and backend fundamentals
- Aptitude for handling ETL pipelines and APIs
Required Skills Explained
- Strong understanding of RAG architecture and embeddings
- Experience with vector databases such as Pinecone, Weaviate, FAISS
- Data preprocessing and structuring skills
- Proficiency in Python and backend fundamentals including APIs, JSON, ETL pipelines
Who is this for
pThis role is ideal for individuals who have a strong background in AI data management, a keen interest in developing scalable AI systems, and experience with vector databases. A passion for continuous learning and problem-solving is essential.
Why This Job is a Good Opportunity
ulliOpportunity to work on cutting-edge AI technologies and systems.liPotential for rapid learning and skill development in areas like RAG and embeddings.liCollaboration with advanced AI platforms and tools, enhancing your technical toolkit.liChance to contribute to projects that can significantly impact the company's operations and services.
Interview Preparation Tips
- Review key concepts of RAG architecture and how embeddings work.
- Prepare examples of data preprocessing techniques you have used.
- Be ready to discuss your experience with vector databases and their integration into retrieval systems.
- PRACTICE coding problems related to Python, APIs, and JSON handling.
Career Growth in This Role
pGrowth opportunities are significant as this role allows you to deepen your expertise in AI data management. With a focus on RAG systems, you can specialize further or broaden your skills into other areas of AI development. Additionally, the hands-on experience with various technologies and platforms provides a strong foundation for advancing to more senior roles within the organization.
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Skills
Frequently Asked Questions
What is the work location?The job is in-person in Delhi.
Is this a full-time or part-time position?This is a full-time position with a contract length of 3-6 months.
What are the KPIs for this role?The key performance indicators include retrieval accuracy, response relevance score, and query latency (response time).
