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Role Overview

Design and optimize prompts for large language models to build robust AI applications. Develop LLM-powered solutions using LangChain and vector databases.

Responsibilities

Design, test, and optimize prompts for LLMs to achieve desired outputs and behaviors
Build and deploy LLM applications using LangChain and related frameworks
Implement RAG (Retrieval Augmented Generation) systems with vector databases
Develop prompt templates, chains, and agents for various use cases
Evaluate and benchmark LLM performance across different prompting strategies
Collaborate with engineers and product teams to integrate LLM solutions

Requirements

Bachelor's degree in Computer Science, Engineering, Linguistics, or related field
Proven experience in LLM prompting and prompt engineering techniques
Strong hands-on experience with LangChain framework
Proficiency with vector databases (Pinecone, Weaviate, ChromaDB, FAISS)
Understanding of LLM capabilities, limitations, and best practices
Experience with OpenAI, Anthropic, or other LLM APIs
Proficiency in Python

Preferred

Experience with prompt optimization techniques (few-shot, chain-of-thought, ReAct)
Knowledge of embedding models and semantic search
Familiarity with LLM evaluation frameworks
Understanding of agentic workflows and tool-using LLMs
Experience with MLOps and production deployment

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Prompt Engineer

Job Type