LLM/Prompt-Context Engineer - Fullstack Python (AI Agents, LangGraph, Context Engineering)

LLM/Prompt-Context Engineer – Fullstack Python (AI Agents, LangGraph, Context Engineering)Location – 1st Atlanta, 2nd Dallas, 3rd Seattle (Onsite no remote). Onsite interview requiredWe are looking for a highly skilled LLM/Prompt-Context Engineer with a strong fullstack Python background to design, develop, and integrate intelligent systems focused on large language models (LLMs), prompt engineering, and advanced context management. In this role, you will play a critical part in architecting context-rich AI solutions, crafting effective prompts, and ensuring seamless agent interactions using frameworks like LangGraph.Key Responsibilities:Prompt & Context Engineering: Design, optimize, and evaluate prompts for LLMs to achieve precise, reliable, and contextually relevant outputs across a variety of use cases.Context Management: Architect and implement dynamic context management strategies, including session memory, retrieval-augmented generation, and user personalization, to enhance agent performance.LLM Integration: Integrate, fine-tune, and orchestrate LLMs within Python-based applications, leveraging APIs and custom pipelines for scalable deployment.LangGraph & Agent Flows: Build and manage complex conversational and agent workflows using the LangGraph framework to support multi-agent or multi-step solutions.Full Stack Development: Develop robust backend services, APIs, and (optionally) front-end interfaces to enable end-to-end AI-powered applications.Collaboration: Work closely with product, data science, and engineering teams to define requirements, run prompt experiments, and iterate quickly on solutions.Evaluation & Optimization: Implement testing, monitoring, and evaluation pipelines to continuously improve prompt effectiveness and context handling.Required Skills & Qualifications:Deep experience with full stack Python development (FastAPI, Flask, Django; SQL/NoSQL databases).Demonstrated expertise in prompt engineering for LLMs (e.g., OpenAI, Anthropic, open-source LLMs).Strong understanding of context engineering, including session management, vector search, and knowledge retrieval strategies.Hands-on experience integrating AI agents and LLMs into production systems.Proficient with conversational flow frameworks such as LangGraph.Familiarity with cloud infrastructure, containerization (Docker), and CI/CD practices.Exceptional analytical, problem-solving, and communication skills.Preferred:Experience evaluating and fine-tuning LLMs or working with RAG architectures.Background in information retrieval, search, or knowledge management systems.Contributions to open-source LLM, agent, or prompt engineering projects.