RAG Prompt Engineer

Job Description

Experience Level: 4 6 Years Location: Bangalore, UB City office Notice Period: Immediate About the Role: We are seeking a skilled and passionate RAG Prompt Engineer to join our AI/ML team. This role focuses on designing, developing, and optimizing prompts and pipelines for Retrieval-Augmented Generation (RAG) systems using Large Language Models (LLMs) . The ideal candidate has a strong background in natural language processing , prompt engineering , and information retrieval , with hands-on experience building scalable LLM-powered applications. Key Responsibilities: Design and implement prompt strategies for RAG-based systems using leading LLM frameworks (e.g., OpenAI, Hugging Face, Cohere). Integrate vector databases and retrieval systems (e.g., FAISS, Pinecone, Weaviate ) with LLMs for accurate and context-aware responses. Fine-tune or instruct-tune LLMs (e.g., LLaMA, GPT-4, Mistral) for domain-specific applications. Optimize query performance and retrieval accuracy in vector search engines. Evaluate and iterate on prompts using metrics such as relevance, coherence, factual accuracy, and latency. Collaborate with product and research teams to deploy RAG pipelines in production environments. Stay up to date with the latest advancements in LLMs, retrieval methods, and generative AI. Required Skills & Qualifications: 4 6 years of experience in NLP, ML, or AI-focused roles, with at least 1 2 years in prompt engineering or LLM application development. Proven experience with RAG architectures and implementation. Proficiency in Python and experience with libraries like LangChain , LlamaIndex , or similar orchestration tools. Experience with LLM APIs (OpenAI, Anthropic, Cohere, etc.) and open-source LLMs (Mistral, LLaMA, Falcon, etc.). Strong understanding of vector search and semantic retrieval using FAISS, Pinecone, Weaviate, or Vespa. Familiarity with prompt tuning, few-shot learning, zero-shot techniques , and evaluation methodologies . Experience with cloud platforms (AWS, GCP, Azure) and MLOps tools for deployment and monitoring. Solid grasp of version control (Git), CI/CD pipelines, and containerization (Docker, Kubernetes preferred).