LLM Prompt Engineer

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 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.