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AI Engineer Weekday. Location Information: India - Remote. . This role is for one of the Weekday's clients. Salary range: Rs 2000000 - Rs 6000000 (ie INR 20-60 LPA). Min Experience: 3 years. JobType: full-time. We are seeking a skilled and motivated AI Engineer with hands-on experience in . Retrieval-Augmented Generation (RAG). to join our growing team. In this role, you will design, build, and optimize AI systems that leverage both large language models (LLMs) and structured/unstructured knowledge bases to deliver intelligent and contextually accurate responses. You will be at the forefront of applied AI, working on cutting-edge solutions that bridge the gap between traditional NLP . pipelines. and next-generation generative AI models.. Requirements. Key Responsibilities:. . Design and develop RAG-based architectures integrating vector databases, document retrieval systems, and LLMs. . . Implement end-to-end pipelines that perform document ingestion, chunking, embedding generation, indexing, and retrieval. . . Fine-tune and optimize retrieval mechanisms using tools such as FAISS, Weaviate, Pinecone, or Elasticsearch. . . Integrate APIs of foundation models like OpenAI, Cohere, or Hugging Face models with retrieval systems to produce context-rich outputs. . . Work with cross-functional teams including ML engineers, data scientists, and product teams to deliver AI-powered features. . . Evaluate model performance using both qualitative and quantitative metrics and iteratively refine system behavior. . . Ensure the accuracy, relevance, and safety of model-generated responses by fine-tuning prompts and optimizing knowledge grounding. . . Stay updated with the latest research in generative AI, RAG systems, and prompt engineering. . . Required Skills and Qualifications:. . 3 to 9 years of experience in AI/ML/NLP with a strong focus on building and deploying generative AI models. . . Deep understanding of Retrieval-Augmented Generation (RAG) concepts and architecture. . . Proficiency with vector databases (e.g., FAISS, Pinecone, Weaviate, Vespa) and embedding generation (e.g., SentenceTransformers, OpenAI Embeddings). . . Strong programming skills in Python and experience with libraries such as LangChain, Transformers, Hugging Face, . PyTorch. , or TensorFlow. . . Familiarity with LLM APIs like OpenAI (GPT-3.5/4), Anthropic, Cohere, or similar. . . Experience working with unstructured data sources (e.g., PDFs, HTML, text files) and knowledge of chunking and context window optimization. . . Ability to design scalable pipelines for real-time or batch inference using cloud platforms (AWS, Azure, GCP). . . Excellent problem-solving, debugging, and analytical skills. . . Strong written and verbal communication skills with the ability to explain technical concepts to non-technical stakeholders. . . Good to Have:. . Experience with LangChain, LlamaIndex, or other retrieval frameworks. . . Exposure to knowledge graphs, search ranking algorithms, and hybrid retrieval methods. . . Familiarity with prompt engineering and few-shot learning strategies. . . Contributions to open-source AI projects or published research in the field of generative AI.. . .