LLM/Prompt-Context Engineer – Fullstack Python

Tips: Provide a summary of the role, what success in the position looks like, and how this role fits into the organization overall. Responsibilities We are seeking a highly skilled LLM/Prompt-Context Engineer with deep expertise in fullstack Python development to design, build, and integrate intelligent systems. In this role, you’ll architect context-rich AI solutions, craft effective prompts, and ensure seamless agent interactions using frameworks such as LangGraph and retrieval-augmented generation (RAG). Key Responsibilities Prompt & Context Engineering • Design, test, and refine prompts for large language models (LLMs) to deliver accurate, context-aware, and reliable outputs across diverse use cases. Context Management • Develop and implement dynamic context strategies — including session memory, retrieval-augmented generation (RAG), and user personalization — to enhance model performance. LLM Integration • Integrate, fine-tune, and orchestrate LLMs into Python-based applications, leveraging APIs, frameworks, and custom pipelines for scalable deployment. LangGraph & Agent Flows • Build and manage multi-agent or multi-step workflows using the LangGraph framework to support advanced conversational and reasoning capabilities. Fullstack Development • Develop and maintain backend services, APIs, and optional frontend components to enable end-to-end AI-driven applications. Collaboration • Partner with product, data science, and engineering teams to translate requirements into technical deliverables, design experiments, and iterate on solutions. Evaluation & Optimization • Implement testing, monitoring, and continuous evaluation pipelines to enhance prompt effectiveness, performance, and contextual accuracy. Required Skills & Qualifications • Strong experience in fullstack Python development (e.g., FastAPI, Flask, Django) with proficiency in SQL/NoSQL databases. • Hands-on expertise in prompt engineering for LLMs (e.g., OpenAI, Anthropic, or open-source models). • Solid understanding of context engineering, including vector search, session memory, and retrieval strategies. • Experience integrating AI agents, LangGraph workflows, and context management systems into scalable architectures. • Excellent communication and collaboration skills, with the ability to work in cross-functional AI solution teams.