Role Summary:

We are looking for a Senior LLM Engineer with deep expertise in Large Language Model development, orchestration, and application engineering. The ideal candidate has 3–5 years of hands-on experience building and deploying AI systems using LangChain, LangGraph, and related frameworks. You will be responsible for designing, implementing, and optimizing complex, multi-agent LLM applications that drive real business value.

Key Responsibilities:

• Design, build, and optimize LLM-based systems leveraging LangChain, LangGraph, and vector databases.
• Develop and maintain modular prompt pipelines, agents, and tools that enable dynamic reasoning and contextual understanding.
• Integrate LLMs with APIs, databases, and internal tools for seamless automation and data interaction.
• Fine-tune, evaluate, and deploy foundation models (e.g., OpenAI, Anthropic, Mistral, Llama) for domain-specific applications.
• Implement retrieval-augmented generation (RAG) and knowledge graph reasoning pipelines.
• Collaborate with cross-functional teams — product managers, data engineers, and researchers — to bring LLM-driven products to life.
• Contribute to LLM system architecture design, ensuring scalability, maintainability, and performance.
• Stay current with emerging AI frameworks, model APIs, and orchestration patterns.

Key requirements:

• Master’s degree or Degree in Computer Science, Artificial Intelligence, or a related field.
• 3–5 years of experience developing with LangChain and LangGraph in production environments.
• Strong proficiency in Python/Java Scripts and LLM APIs (OpenAI, Anthropic, Hugging Face, etc.).
• Experience with RAG pipelines, embeddings, and vector databases.
• Solid understanding of prompt engineering, LLM evaluation, and tool/agent design.
• Experience with Docker, Git, and modern cloud environments (e.g. Azure or AWS etc.).
• Experience with multi-agent systems, function calling, and graph-based orchestration.
• Familiarity with LangSmith or LLMOps tools for observability and performance tracking.
• Knowledge of data pipelines, ETL systems, or microservice architectures.
• Prior work in AI product development, chatbot orchestration, or automation systems.