LLM Engineer

Our client, an international AI development company based in New York, is currently seeking a "LLM Engineer (RAG, VectorStores, and Protocol Engineering)" 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