Overview
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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 fieldProven experience in LLM prompting and prompt engineering techniquesStrong hands-on experience with LangChain frameworkProficiency with vector databases (Pinecone, Weaviate, ChromaDB, FAISS)Understanding of LLM capabilities, limitations, and best practicesExperience with OpenAI, Anthropic, or other LLM APIsProficiency in PythonPreferred
Experience with prompt optimization techniques (few-shot, chain-of-thought, ReAct)Knowledge of embedding models and semantic searchFamiliarity with LLM evaluation frameworksUnderstanding of agentic workflows and tool-using LLMsExperience with MLOps and production deploymentSeniority level
Entry levelEmployment type
Full-timeIndustry
IT Services and IT Consulting#J-18808-Ljbffr