LLM/Prompt-Context Engineer

We are looking for a highly skilled LLM/Prompt-Context Engineer with a strong fellslack Python background to design, develop, and integrate intelligent systems focused on large language models (LLMs), prompt engineering, and advanced content management In this role, you will play a antical part in architecting context-rich Al solutions, crafting effective prompts, and ensuring seamless agent interactions using trameworks 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. • LEM Integration: Integrate, fine-tune, and orchestrate LL Ms 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 Lang Graph framework to support multi-agent or mole step solutions. Eullstack 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 improve prompt effectiveness and content handling continuously Required Skills & Qualifications: • Deep experience with full-stack, Python development (EastART Flask, Django; SQL, NoSQL databases). • Demonstrated expertise in prompt engineering for LIMs (e.g., OpenAI, Anthropie, open-source LLMS). . Strong understanding of context engineering, including session management, vector search, and knowledge retrieval strategies. Hands-on experience integrating Al 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 for working with RAG architectures • Background in information retrieval, search, or knowledge management systems. • Contributions to open-source LLM, agent, or prompt engineering projects.