Prompt Engineer

4 weeks ago


New Delhi, India Intellectt Inc Full time

Prompt EngineerExperience: 3–5 yearsLocation: Hyderabad (Onsite/Hybrid)Job Description:We are looking for a Prompt Engineer with strong software engineering expertise and hands-on experience working with Large Language Models (LLMs) and Agentic AI frameworks. The ideal candidate will design, test, and optimize prompts, retrieval pipelines, and agentic workflows that power intelligent, production-grade AI systems.Key Responsibilities:- Design, prototype, and refine prompts for LLMs (OpenAI, Anthropic, Gemini, Llama, etc.). - Build and integrate LLM-based services via APIs with FastAPI (routers, DI, auth, rate limiting). - Implement Retrieval-Augmented Generation (RAG) workflows — document parsing, chunking, embeddings, vector search, and hybrid retrieval. - Develop agentic flows and state machines using LangGraph; build chains/tools using LangChain. - Create and maintain testing frameworks for prompts, retrieval logic, and chain/tool performance (A/B testing, relevance, latency, and cost). - Manage vector database integrations (FAISS, Pinecone) and ensure retrieval accuracy. - Collaborate on end-to-end API development, CI/CD integration, and system monitoring. - Maintain documentation, best practices, and advocate for prompt engineering standards across teams.Must-Have Skills:- 3–5 years of software engineering experience with strong Python skills. - Proven hands-on experience with FastAPI, LLMs (OpenAI, Anthropic, Gemini, Llama), and function/tool calling. - Practical experience implementing RAG, embeddings, hybrid retrieval, and document chunking. - Proficiency with LangGraph and/or LangChain for agentic and chained workflows. - Experience with vector databases (FAISS, Pinecone, Weaviate) and document preprocessing. - Familiarity with Git, Docker, and CI/CD pipelines. - Strong communication and collaboration skills.Nice-to-Have Skills:- Experience in LLM fine-tuning (LoRA, quantization) and monitoring prompt performance. - Knowledge of cloud deployment (AWS/Azure/GCP) and LLM observability (latency, cost tracking). - Prior exposure to regulated domains such as healthcare or life sciences.


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