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 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 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 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 #J-18808-Ljbffr