LLM/Prompt-Context Engineer
LLM/Prompt-Context Engineer
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1 Vacancy
Job 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 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.
Employment Type
Full-time
