LLM Engineer

Our client, an international AI development company based in New York, is currently seeking a " **LLM Engineer** " to lead strategic product development efforts in a fast\-paced and collaborative environment. This role will focus on implementing scalable vector store integrations, building retrieval pipelines, and enabling advanced communication protocols between intelligent agents. **Key Responsibilities** RAG \& VectorStore Systems: Build and maintain end\-to\-end RAG pipelines for context\-augmented generation Integrate and optimize vector databases (FAISS, Pinecone, Weaviate, Milvus) Support the engineering backbone of agentic and RAG\-based AI systems Protocol Engineering: Implement Model Context Protocol (MCP) to maintain stateful LLM interactions Build Agent\-to\-Agent (A2A) communication layers for multi\-agent orchestration Enable persistent memory and context sharing across model calls Platform Enablement: Collaborate with cross\-functional teams to productionize models and workflows Ensure seamless data flow and model integration across services **Qualifications \& Skills** Deep experience with vector databases and RAG architecture Familiarity with MCP (Model Context Protocol) and A2A (Agent\-to\-Agent) design patterns Solid background in Python, cloud\-based ML pipelines, and containerization tools Experience in operationalizing LLM\-based systems in production Detail\-oriented with a strong engineering mindset Effective communicator with technical and non\-technical stakeholders Self\-driven and adaptable in a fast\-paced R\&D environment Very strong English communication skills, both written and verbal (essential for global collaboration) Experience with Contract Analysis and automated document understanding Nice to Have: Experience with invoice parsing / invoice understanding, including extracting structured data from financial documents Experience in NLP\-to\-SQL, natural language querying, or generating structured database queries from unstructured text Familiarity with legal\-tech, document intelligence, or enterprise knowledge extraction systems Experience with observability, vector hygiene, and evaluation frameworks for RAG/agentic systems Hands\-on work with high\-throughput, low\-latency AI pipelines