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).
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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.