Prompt Engineer

1 day ago


Hyderabad, India Intellectt Inc Full time

Prompt Engineer Experience: 3–5 years Location: 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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