Full Stack Python with LLM Engineer

Role: LLM/Prompt-Context Engineer – Full Stack Python (AI Agents, LangGraph, Context Engineering) Location: Atlanta, GA/Seattle, WA/Dallas, TX Type of Employment: Contract Note: Desired applicant needs to attend an In-person in any of the above locations Description: We 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. • Fullstack 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 fullstack 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.