RAG Engineer
Profile Code: AL-AI-05
- ₹24 LPA (Median Salary)
- Lecture Duration 2hrs
- Course Duration 20 Weeks
Skills You Learn: Retrieval-Augmented Generation (RAG) | Large Language Models (LLMs) | Vector Databases & Embeddings | Semantic Search & Information Retrieval | Prompt Engineering & Context Management | Python & API Integration | LangChain, LlamaIndex & AI Frameworks | Generative AI Deployment & MLOps
Overview Video
About This Course
RAG Engineers design, build, and optimize Retrieval-Augmented Generation (RAG) systems that combine large language models (LLMs) with enterprise knowledge sources to deliver accurate, context-aware, and reliable AI responses. They develop intelligent applications by integrating vector databases, document retrieval, embeddings, and AI orchestration frameworks to solve real-world business challenges. This course prepares learners for a career as a RAG Engineer through hands-on projects, industry workflows, collaborative learning, and real-world case studies aligned with current hiring expectations in India. Learners gain expertise in Python, Retrieval-Augmented Generation (RAG), LangChain, vector databases, embeddings, prompt engineering, AI agents, document processing, API integration, and responsible AI practices. The curriculum includes practical experience with leading AI tools and cloud platforms. Through live projects and capstone assignments, participants develop programming, analytical, and AI application development skills, preparing them for RAG Engineer, AI Engineer, LLM Engineer, and AI Solutions Developer roles across diverse industries.
Key Responsibilities
- Design and develop Retrieval-Augmented Generation (RAG) systems that combine large language models with external knowledge sources
- Build and optimize document ingestion, indexing, and retrieval pipelines using vector databases
- Implement embeddings, semantic search, and context management to improve AI response accuracy
- Integrate RAG applications with APIs, enterprise systems, and knowledge repositories
- Monitor, evaluate, and optimize RAG performance, scalability, and reliability in production environments
Growth Path
Tools Used
Perfect For
Computer Science and Engineering Graduates | Software Developers and Backend Engineers | AI and Machine Learning Enthusiasts | Data Scientists and NLP Professionals | Knowledge Management and Search Engineers | Individuals Interested in Building Enterprise AI Applications
Fee Structure
Mentor
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